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Audio Direction Finding, Dissertation Example

Pages: 43

Words: 11818

Dissertation

Abstract

An audio source algorithm was introduced for the underground parking areas. The algorithm involved calibration for the module of the audio directional finding that had been founded on the application of the least squares method (LSM). The recommended localization of the audio sources was proposed for the underground parking areas in metropolitan apartment and office structures. The algorithms calculated the origin of the sounds from vectors of   45 °, 90 °, 135°, 180 °, 225 °, 270 °   and 315 °. The audio origin localization system was designated as the audial source detection system (ADS). The calibration system that had been applied for the ADS was a calibration system named ADS’. The recommended calibration decreased the uncertainty with regards to the origin of the audial sources by an average of 75%. The aim of the research is to apply the practical application of the audio source detection systems and the calibrating algorithm to the underground video security systems of the underground parking areas which are found min office complexes and apartment buildings. The ADS and the ADS’ algorithm can be applied to the determination of the audial sources in underground apartment building parking areas in addition to a variety of other applications based on its cost avoidant quality [1, 19].

Keywords: audio source detection, audio direction finding, calibration, module, algorithm

Background

Underground parking areas in apartment buildings are havens for trespassers who have   intentions of conducting illicit activities. The residents as well as the members of the community have significant safety preoccupations with regards to traveling through the underground parking areas or parking their vehicles. The application of video monitoring is normally applied in these circumstances in order to provide the residents with a perception of safety. The visual monitoring systems can provide cues to the security services or to the police departments in order to dispatch personnel in order to deter any harm from coming to the victims. Notwithstanding, there are areas of the scope of the video monitoring camera system which can be designated as blind spots. The aim of the research is to apply the practical application of the audio source detection systems and the calibrating algorithm to the underground video security systems of the underground parking areas which are found min office complexes and apartment buildings. The perpetrators of criminal activities are cognizant of the video monitoring systems’ blind spots. The blind spots are positioned in a manner where even an array of video monitoring cameras are not able to administrate. This is attributed to the cameras inability to focus on the targets which are not in the center of their visual range. Consequently, the cameras serve as a form of assurance for the residents and passersby. However, the cameras are not the most optimal manner of scanning the areas with regards to deterring illicit activities. [1]

Sound and Audio

Considering the requisite of mitigating the issue of blind spots in the apartment and office building underground parking areas, the technological perspectives which include audial source recognition, have been presented by the socially responsible institution of higher learning with regards to IT. There is a user audio phone video surveillance system which has been promoted by INTEC. The surveillance systems can be affixed to the user audio reception device that is located in a security office. This would further deter potential perpetrators from committing illicit activities in the underground parking areas of office buildings and apartments [1, 18].

Microphones

The majority of the apartment and office parking areas have video surveillance devices which do not gyrate. The deficiency in gyration is administrated by the application of four microphones. In the application of the four surveillance cameras, there are blind areas which are not accessible by the video cameras as a result of the elevated expense of maintaining the gyrating cameras. Consequently, the microphones are required in order to provide the gyration cues for the video surveillance system [5, 6]. The acquisition of the audio direction are conventional challenges with regards to the microphone array processing. This field has been intensively reviewed with regards to the application of microphones which were distanced. The state of the art formulas have a restricted capacity of three dimensional acoustic origin determination. The real time audio directional finding has proven to be one of the objectives for the United States National Academy of Engineering [22].

Direction Finding

It is perceived that the ascertaining of the audio sources can be modified in order to provide enhanced security for the underground parking areas of the office and apartment structures. This can be performed with the assistance of the application of special dampening materials in the floor of the underground parking garage. These dampening materials would minimize the effect of the reverberations and enable the more accurate discernment of the position of the audio sources. The dampening material would minimize the reverberations that are experienced as the automobiles ingress or egress from the underground parking areas. The recommended algorithm for the determining of the location of the audio sources can be modified in order to filter the sounds of the automobiles which ingress and egress. The determining of the audio sources in underground parking areas can be quite effective. This technology has the potential of enhancing the safety of the passersby, including the occupants of the office and the apartment buildings. The application of audio directional locating devices is relatively cost avoidant in comparison to the conventional security surveillance methods [2, 3].

History of Direction Finding

It has been acknowledged that the determination of the location of the audio sources can be effective for the armed forces with regards to the ascertaining of an enemy vessel’s   whereabouts or the location of enemy ground forces. The technology of applying audio detection is fairly new. There have been some commercial devices which have been marketed. These devices are usually made available to the armed forces rather than to the public consumers [18].  The scientists at the ADSC (Advanced Digital Science Center) have shown the capability of being able to ascertain the source of audio emissions in three dimensions. This has been achieved in real time by the application of a miniature microphone arrays. The approach that has been applied by the scientists at ADSC has demonstrated the ability of audio directional fining within a circular area of 2° x 2°. This audio directional fining localization represents a substantial level of progress from the research that had been previously conducted [22].

Applications and Importance of Direction Finding

Notwithstanding, the indexes of uncertainty of the determination of the position of the audio sources in the absence of a calibration algorithm is has been often found to be elevated in the real world applications. Consequently, the audio directional systems calibration algorithm is indispensable in order to increase the production of the locating precision. In order to evaluate the recommended algorithm’s capacity of correlating of the audio origins, the audio source analyzer and receiver has been developed. In this module is where the recommended algorithms are realized. The suggested locations of the audio source systems combined with the calibration algorithm has the capacity of administrating the surveillance camera to gyrate to the assessed orientation [3, 4].

In this research, the determination of audio sources formula is recommended for the underground parking areas of the apartment buildings and office structures. The audio sources formula will be applied in collaboration with the calibration algorithm that is founded upon the least squares method (LSM).[7] Furthermore, there is a comparison of the production of the recommended algorithms with the application of the data that is derived from the experiments conducted in the parking areas in the methodology section. The audio source that is determined is used in order to provide coordinates for the gyration of the video surveillance cameras in the areas that are presently inaccessible due to blind spots [5, 6].

Chapter 2 Literature Review

Key Concepts

The formulas that include the Taylor series, the temporal differential of arrival, the hyperboloid algorithm and the separating of spaces algorithm are applied for determining the position of the audio sources. The determination of the audio sources by means of applying the hyperboloid algorithm provides an evaluation of the position of the point of intersection by resolving three hyperboloid algorithms from the three characteristics of directional evidence. This is performed by the application of four USB microphones which applied four distinct equations. The hyperboloid equation is ascertained by the application of the distance which is assessed from the temporal differential. The modeling that is founded upon the three dimensional paradigm of the hyperboloids with regards to the positioning of the sound origins [4, 11].

Direction of Arrival

The determination of the audio origins by the application of the temporal difference of arrival (TDOA) has the quality of basically evaluating the position by the measurement of the temporal interval between the signals received by the USB microphones in a particular zone. The information that is derived from the TDOA provides a specialized implication of positioning the audio sources by the application of the Taylor series. The Taylor series facilitates the solution of the least square methods with minimal error with regards to the resolution of a collection of synchronous algebraic linear equations. This model is a frequentative plan for the synchronous collection of equations that apply algebraic positioning. These equations usually initiate with an approximation. The approximation improves each time by ascertaining the error correction that is most proximate for the least squares aggregation method [7, 12].

Angle of Arrival

A communication audio link security surveillance system was applied in order to differentiate the sound that emerged from the alarms which had been activated. In addition, the alarm was reported to the security authorities. The matrix and the system of the invention is dependent on the audio sound that is produced from the alarms which have been activated. The system may be applied on a PC or any other type of processor. The sensors differentiate the sound from the distinct categories of alarms and conveys the alarm signal by means of the internet or other type of communication link to the centralized station. The centralized station can then convey the characteristics of the communication to the corresponding authorities. The documenting of the alarm condition can be realized by the sensing devices and processed by the PC or other processing components. This type of invention is highly applicable in the home security field. The home security market has been demonstrated to be a substantial market which is increasing at the rate of 14% annually. This market is composed of the various audio signals that are associated with personal emergence first response alarms, glass shattering alarms, burglar alarms and fire alarms. These alarms have the characteristics of the audio directional finding and can be conveyed to a processor. The processor can then convey the information to the proper authorities or can dispatch a signal to the video surveillance system in order to gyrate the camera viewing angle toward the area that requires increased surveillance and attention [10].

Frequency of the Signals

The more elevated the frequency of the audio events, the more elevated the indexes of errors in the sampling rates. An additional algorithm was found to be required in order to minimize the low sampling index in order to ascertain the elevated frequency audio sources [16]. There has been substantial research that has been conducted with regards to the production of an audio orientation finding microphone. The theory that is the foundation of this implementation which optimizes the fundamental characteristics of the apertures that are correlated by derivative was established by researcher at the UT at Austin. A hydrophone paradigm was implemented by researchers. An audio frequency microphone is being developed that is founded upon these theories which operates in air [23].

Beamforming

The main part of the application of the audio directional finding is to locate the acoustic source by means of cost avoidant sensor nodes. In the previous approaches distributed acoustic source sensor nodes were applied which is the disseminated acoustic source locating algorithm.  In the application of DSL, the sensor nodes communicate the temporal intervals of audio detection in the event that the present setting is at a more elevated level than the threshold. The location of the audio source is tabulated by means of using the sequence of detection times in the sensor nodes. The DSL has the capacity of being able to conduct audio direction positioning, notwithstanding, the audio qualities. This is attributed to the mechanisms application of time information. The DSL system does not have the requisite of any additional hardware with the exception of an inexpensive USB microphone, in comparison with beamforming or tabulating the angles of arrival [13, 14].

The electro- magnetic models of assessment of the orientation of the origin of an EM signal can be applied to audio directional finding. The information that is derived from two or more of the appropriately located receivers can be applied to triangulation. This type of model is applied in the navigation of aircraft and maritime vessels. Furthermore, the triangulation of EM signals is applied for the determination of the emergency transmitters which are applied in search and rescue operation.

The radio directional finding matrices can be applied with any radio origin. The dimension of the antennas are a function of the characteristics of the signal’s wavelength. The lower frequencies of radio waves have the requisites of larger antennas which are basically applied in system that are terrestrial. The longer wavelengths have the characteristic of being applied in maritime applications as the wavelengths have the ability of traveling past the line of sight. In the aerial applications, the line of sight is much greater. Consequently, the aerial antennas can apply a shorter electro- magnetic wavelength.  The automated directional finders were once applied in order to synchronize with the frequency of the beacons of the modulated amplitude of radio broadcast on aircraft. The radar systems which are applied work on a similar paradigm as the electromagnetic waves. The radar is able to discern the direction of the audio signal as an outcome of the direction of the antenna [24].

Sampling

In the intelligent underground parking area surveillance system, the sensor nodes which are microphone enabled are affixed to the ceiling of the underground parking areas. These sensors would have the capacity of sampling the sound at indexes of 3- kHz. The DSL (distribution acoustic source localization) operate on reach of the sensor nodes in order to ascertain the audio direction of the events. In the video camera surveillance systems, a collection of cameras are usually applied in order to administrate an underground parking area. In the consideration of the diverse surroundings for the installation of the video devices, the cameras which were applied possessed wireless and wire network interfaces. The surveillance camera system which administrates the direction of the gyration of the camera applied the information derived from the audio directional location in order to position the camera’s focus. The final position is calibrated within the algorithm that is applied. The supervisor is informed of the event by the surveillance servers through the streamed response video that is derived from the video system, which monitors and reacts to the situation by means of the audio directional input [17, 18].

Fast Fourier Transform

The audio directional source algorithm which is founded on a rare Fast Fourier Transformation option which features the spatial dispersion and the extracting matrix are suggested. In this research, the audio sources are delineated by separating the space in a discrete fashion by representing the area in a circular matrix. The research that had been conducted in [12] shows that the LLS (linear least squares) approach in addition to the SWLS (dual stage least squares approach). These two matrixes have similar assessment perspectives which become ineffective when the sensor has a geometrical quality of a homogeneous circular array where the audio source in in close proximity to the core of the array. Furthermore, a novel restricted average least squares approximation is recommended in order to diminish the challenge in the research [7, 12].

Array Signal Processing

Research that had been conducted by means of a distributed questionnaire with regards to the diverse passive positioning approaches and the active positioning approaches that include the receiver’s strength signal indicator) RSSI, TOA (temporal indicator of arrival), TDOA (temporal distance of arrival) and the  AOA (angular incidence of arrival) was assessed [8]. The consensus of the audio directional finding techniques in this research are components to the category of the active systems which are monitored or placed on items for the approximation of the locality. A microphone array for the real – time audio directional source is proposed in a hybrid matrix [9]. The central idea is the application by means of the temporal distance of arrival which is founded on GCC (general cross correlates) with the objective of specifying the search area of the directed response power phase transformers (SRP- PHAT). It is observed that the directed response audio directional finding formula have the tendency of not being capable of ascertaining the audio source  in the challenging and reverberation circumstances when the straight routes to the microphones are not accessible. The research suggests a localizing formula which is founded on the distinguishing of the cross correlating functions for the resolution of this problem [10].

Radio Directional Finding

An outline of the assessment approaches in the sensor network positioning and the singular jump positioning algorithms which are founded on the multi hop connections and the distance orientated localization algorithms is presented by [5]. A novel approach for the positioning in the WSN (wireless sensing networks) founded on the administrated Laplacian normalized least squares approximation is recommended in [6]. This perspective takes into consideration the two categories of positioning data. These two categories of positioning data are the paired proximity that is present between the hubs and the robust quality of the signal. In [7], the function that is introduced is the kernel functioning. A system for the free range positioning by means of the application of nonlinear coordinates with regards to the wireless sensing networks is presented [8]. The positioning challenge in the wireless sensing network is manifested by a mathematical equation that has qualities of the kernel’s regression. This type of equation is approximated by the application of MSVR (multidimensional supporting vector regression and SVR (supporting vector regression) [9].

Radio Direction Finding

There have been several approaches that have been reviewed which are DSL, MBFM and AML. The AML applies the combination of DOA and beam forming in order to monitor a variety of audio sources and has the requisite of supplementary hardware on the IEEE802.11 network. Furthermore, the overflow of the network that could take place in the targeted zone is greater than the range of the network. The cost avoidant sensing nodes do not have the ability of realizing an extremely complicated algorithm, each of the nodes has the requisite of conveying the data to the base station, which has the capacity of conducting the tabulations for the conclusive localization. This category of centralized approach conventional develops network overhead and the precision of the system is diminished [11, 13].

Using Directional Antennas

The generalized audio source localization matrixes are categorized into the beam formed directed systems and the directed arrival (DOA) systems. The directed arrival systems follow the audio sources by ascertaining the direction of the audio direction with a collection of microphones that are attached to one sensor node. Consequently, the directed arrival based systems have the requisite of more robust component in order to administrate the high speed sampling index. The administration of the high speed sampling index is a requisite for the differentiation of the detection intervals of the microphones. The beam formed directed systems have the ability of monitoring the audio origins by making a comparison of the waveforms of the audio origins. The beam formed directed devices have the requirements equipment which have a more powerful quality, which include the PDA in order to compute the substantial amount of information [13].

Time Difference of Arrival

The MDSL applies an audio directional fining origin which is founded on the TDOA formulas. Consequently, the precision of the audio directional finding time is an important characteristic in the determination of the precision of the MDSL. In the related research, there had been experiments conducted with regards to the temporal uncertainty in MDSL. There were two nodes which were implemented in similar locations and an audio source was applied at a 5m distance from each of the nodes. The difference was assessed in the detection period of the two nodes. The maximum differential was evaluated to be 19,600 µs in twenty trials which is substantial with regards to causing a measurable localization uncertainty. The temporal uncertainty was reviewed. This was attributed to the premise that the uncertainty could have been attributed to the estimated time of arrival algorithm. The two nodes were subsequently placed at similar positions. The temporal distinction of the two nodes was subsequently assessed to be smaller than 30 µs in the twenty trials, which was not significant and would not influence the precision of the MDSL [13, 17].

Watson- Watt

The Watson- Watt is a system which applies the comparison of amplitude in the DF matrix. The Watson- Watt applies the crossed loop or Adcock system in order to provide a comparison of the signals that are transmitted to each of the antenna. The device tabulates the bearings based on the signal distinctions. The crossed ferrite loop antenna possess four equidistant vertical components. These components are positioned at each of the cardinal direction s. The crossed loop antennae are configured in pairs along a plane. These pairs would include the East – West configuration and the north – south configuration. The outcome is two lobes which have the quality of two perpendicular figure eight forms that manifest optimal sensitivity on the nulls and the axis which are perpendicular with the axis. The configuration has the characteristics of creating a distinct collection of magnitudes for each of the orientations as a result of the antenna configuration. The crossed loop antennas can conventionally be applied to vehicles or to dipoles which are applied for configurations that have the requisite of tower applications. The distance that is manifested in the configuration of the aerials enables greater accuracy. The closer the proximity of the crossed loop antennas, the higher the precision. The greater the distance between the crossed loop antennas, the higher the sensitivity [20].

Doppler Direction Finder

The audio sources positioning algorithm is based on the determining of the audio origins in the underground parking areas of apartment and office complexes. The need for accurately determining the sound origins may not be substantial. The need may be derived from being able to distinguish the noises which are produced by the ingression or egression of the automobiles and being able to filter these sounds from other sounds. In the event of the sounds which do not originate from the ingress or egress of the automobiles, the sound may be applied in order to activate the gyrating option of the video monitoring systems in order to direct increased attention to the audio directional origin. The emphasis of the audio directional finding system in the underground parking areas can be directed at the blind spots [16].

The fundamental causal attribute of the audio directional positioning of sound origins in the underground parking areas is the ready detection of the sounds which are not associated with the ingression or egression of automobile traffic. There are a number of audio sources which include automobile motion and pedestrian traffic. The specialized coating that is placed upon the floor of the automobile underground garage would cause the automobile traffic to be distinguished from other noises. The coating would cause the amplification of the automobile sounds which originate from the contact of the tires on the pavement [15].

The operating surroundings of the recommended formula takes into consideration that the automobiles are entering or egressing sequentially in the underground parking areas. Notwithstanding, there may be multiple automobiles which egress or ingress simultaneously. The underground parking areas frequently have more than one level and are associated with one another by means of corridors. Consequently, if the audio directional finder encounters the sound that originates from an automobile in motion, a signal would be conveyed to the video surveillance monitors in order to examine the vehicle in greater detail. The recommended audio directional positioning of the audio origins can be modified to function at the intersections on one of the underground avenues in the underground parking areas [1, 15, and 19].

Interferometer

The operation of the interferometer is composed of two stages. The phase distinction are tabulated with regards to the reception of the signal at multiple antennae which are collocated. The assessed phase distinctions are evaluated with regards to a reference information database. The reference point is acquired for a DF matrix which has a predetermined configuration and a recognized angle of transmission. The reference table is applied in order to interpolate the information for greater precision. The conventional uncertainty is less than ± 1°. The quality of precision that is manifested by the interferometer is more efficient than the other DF approaches with regards to multipath fading and the external noise interference for correlated antenna diameters. The interferometer enables the assessment of elevation. In addition, the antennas have longer ranges. The interferometer response interval is better than the systems that apply the Doppler Shift matrix. This is attributed to the premise that the production of the amplitude and frequency are sampled synchronously instead of sequentially [21].

The localization approaches for the positioning of the vehicles and the evaluation of the manner by which these approaches may be merged is reviewed in [11]. The approaches that are applied by means of the data fusion perspectives in order to supply the confirmed localization matrix in the VANets (vehicle ad- hoc localization networks) are discussed [15].  The fuzzy logic audio directional positioning systems with regards to the audio signal magnitude in the underground parking areas is recommended, with the production being evaluated for the precision and stability of the audio directional algorithm. It can be observed that the production of the matrix is 400% the production of the general audio directional positioning algorithm [19]. The calibration algorithm for the application of the least squares method in areas where there is no direct line of vison is suggested and the production of the formula is reviewed [15]. In this research, it is documented that the audio directional positioning accuracy of the formula is more elevated than the triangulation design by an average of 86%. Considering the application of the Kalman filter, the average is as elevated as 16%. The research that was conducted in [15] considers the application in the indoor and the outdoor surroundings and does not provide consideration to the underground parking areas of apartment and office buildings.

Direction Finding Using Sensor Array Processing

The systems which had been previously applied required non cost avoidant components which included supplemental sampling circuits or a state of the art microprocessor in order to acquire the elevated levels of sampling indexes. In the DSL algorithm that is applied in underground parking areas of the office and apartment complexes, the sensor nodes observe and collaborate on the senor time in order to choose a leader node which would manifest the earliest time of detection. The leading node tabulates the essential events region (EER).This is the potential source of the audio direction and origin. The nodes review which of the cells the EER possesses the most elevated probability of ascertaining the acoustic source location by collaborating and comparing the times of detection [13, 18]. Consequently, the outcome is conveyed to the leading node. The leading nodes assesses the source origin and direction with the data that had been collected.

Instead of this system, the original system had been applied with regards to the DSL. The initial experiments which were conducted at the Gateway Center Union Station underground parking lot and the Avalon Oak Creek apartment’s underground parking lot in Los Angeles did not demonstrate the anticipated production. The problems were reviewed and there had been several sources of uncertainty that had been discovered in the experiments. The primary source of uncertainty was the decreased sampling index of the sensor mode [1, 16 and 19].

As the USB microphones which had been on the edge of the circular array in the underground parking areas were affected with the deficiency of information with regards to precisely approximating the location of the audio source, the DSL demonstrates an inferior production at the corners than it does in the midst of the sensor array. The conclusive source of uncertainty is the tenure of the audio event. The initially applied DSL was conceived in order to discover sound events which had a short duration, consequently, an acoustic event which possess a long duration is perceived as a number of smaller events. The reiterative detection of an audio event may cause the subsystem to waste energy. In addition the distinct points of initiation of the proximate sensing cycle have the potential of being an origin for uncertainty. The distinct points of initiation of the proximate sensing cycle have the capacity of causing jitters, which can be substantial sources of approximation error [13, 16].

In order to mitigate this phenomenon, the recommended systems deters the sensor nodes from sensing the proximate audio events. The node which is assessing the estimated location provides the information to the server while continuing to sample the conclusion of the audio event. In the event that none of the audio events are present which exceed the systems threshold for the predesignated temporal interval, the audio event is assumed as having concluded. A communication is subsequently conveyed in order to initiate the sampling of the neighboring nodes [11, 13].

Bearings

In DSL the hub which has the earliest time of detection is selected as being the leading node. The leading node has the function of selecting the event detection periods from the proximate nodes. As the EER is formulated founded on the data which has been gathered, the audio directional distribution of the proximate nodes is of great significance in order to determine the location of the audio source. The production of the DSL may vary reliant ion the location of the audio source. In the event of the audio source is taking place in the center of the circular array, the location can be ascertained with a low index of uncertainty. In the event that the audio event takes place on the perimeter of the circular array, the positioning of the audio source becomes more challenging due to the deficiency in the data required to estimate the location [13, 18].

In addition, the inherent uncertainty in the detection times which was attributed to the decreased sampling index of the sensor that was applied in MDSL was assessed. The detection time on the corresponding node decrease the audio directional finding precision of the MDSL. In order to compensate for this challenge, a detection algorithm was produced, which applied the two indexes of the threshold as a buffering strategy. In the event that the value obtained by the sensor was more elevated than the initial threshold, the system sought a buffer in order to encounter a more precise detection time. The detection interval was supplanted by the correlated assessment time of the sensor’s value, which was the initial sensing value that was more elevated than the subsequent threshold which had been in the buffer. In the application of this algorithm, the time difference in detection was substantially reduced.

In order to be able to compensate for the lack of data with regards to events that take place on the edge of the circular array, the recommended algorithm takes into account the sequence of the event detections in addition to the time differential between the activation of the nodes. The initial DSL applies the temporal information in order to determining which of the nodes was able to detect the event first. The present algorithm applies the distinctions in the times of the event detections.  The algorithm functions in the following manner:

  1. The EER of the leading node is to be tabulated by means of the application of the DSL algorithm. The detection times of the nodes were distinguished.
  2. The EER is separated into discrete units which composed on parking space in the underground parking areas. The following processes was conducted at each of the regions:
  3. The distances tabulated were from the nodes to each of the sub regions.
  4. In the event that the sequence of the distances does not fulfill the sequence of the occurrence, the sub region should be removed from the EER.
  5. The position of the audio origin should be determined by taking a weighted average of the remnant EER.

The microphone threshold indexes for the system were considered. This was accomplished by the assessment of the microphone indexes for the diverse audio events in an underground parking area. The assessed values were derived from the analog digital converting unit and the readings which were presented. The table demonstrates the values of the audio events.

Table 1: Sensing Measurements of the Audio Events in an Underground Parking Area

Automobile Alarm Car Door Slamming Dialogue Automobile Movement
Decibel Intensity (dB) 105- 125 90- 110 65- 70 75- 85
Information from the ADC 75- 3827 536- 3502 1780- 2050 1715- 2291

The threshold of the microphones were established at an ADC value of between 1760 and 2400 in order to provide fluttering for these sounds. In the event that the slamming of an automobile door triggers one of the microphone sensors, the audio event was manually determined by means of ate application of the video surveillance system.

Chapter 3- Experimental Procedure and Materials

In order to attain the objective which have previously been delineated, an audio direction finding algorithm has been designed in order to conduct four tasks. The first task is the assessment of the audio sources. The second task would be the evaluation of the magnitude of the audio sources. The third task is the tabulation of the characteristics of the audio sources. The final task is the audio directional finding of the audio sources [1, 19].

In the initial procedure, the assessment of the audio sources, the audio signals that are received from the four USB microphones are determined and evaluated. The four microphones would be equidistant with respect to one another. The spatial differential of the microphone would be positioned in a circular array with positioned at 90 °, 180 °, 270 ° and 360 °. These USB microphones would have the capacity of evaluating the audio orientations of right, left, back and front. The procedure that is involved in the assessment of the audial characteristics would be subsequently conducted by the algorithm with a temporal interval that would be equivalent to one second [1, 19].

In the second procedure, the assessment of the magnitude of the audio origins and the values which are more substantial than the threshold values (TV audio magnitude) within the received inputs would be tabulated by applying the following formula:

Rating audio magnitude≥ Threshold rating audio magnitude

Subsequent to the application of the formula, the assessments to the ratings were applied in the second formula:

Measurement (Rating audio magnitude) = Measurement (Rating audio magnitude) + 1

The threshold rating for the audio determination of the automobiles in motion would be ascertained to an appropriate level. The threshold rating index is reliant on the surroundings and the noise produced from the surroundings in the underground parking area [1, 19]. Consequently, the additional noises which included the movement of the automobiles as the tires make contact with the pavement of the underground parking area were excluded.

In the third task, the tabulation of the audio origins, only the greatest measurement values were included (Measurement (Rating audio magnitude)) and had been chosen among the four audio inputs that are acquired. In the formula

Measurement (Rating audio magnitude) = Measurement (Rating audio magnitude) + 1

This included the calibrated angle which would be tabulated between the two microphones that had been chosen. The value for the calibrated angle is demonstrated as Δ θ(x, y). The smallest measurement between the measurement MIC(x) and measurement MIC(Y) was chosen and delegated to the numerator in the right parts of the formula demonstrated [1, 19].

Δ θ(x, y) = │Δ θ MIC (X) – Δ θ MIC (Y) │ x (measurement MIC(X) or measurement MIC(Y)) x (measurement MIC(X) + measurement MIC(Y))

In the fourth task, the audio direction positioning approximation of the audio sources, the angular position of the audio sources was estimated by the following formula:

LOC Audio Source = Δ θ(x, y) + Δ θ MIC (X)

In the event that Δ θ MIC (X) < Δ θ MIC (Y)

LOC Audio Source = Δ θ MIC (X, Y) – Δ θ MIC (X)

In the case that Δ θ MIC (X) > Δ θ MIC (Y)

If Δ θ MIC (X) < Δ θ MIC (Y), the direction of the audio sources was tabulated by aggregating Δ θ(x, y) in addition to Δ θ MIC (X). In the event that Δ θ MIC (X) > Δ θ MIC (Y), the audio direction was computed by performing the subtraction from Δ θ (X, Y) [1, 19].

In order to modify the recommended audio directional algorithm to the determination of the audio sources matrix, the video camera that had the potential of being gyrated to the complete span of the underground parking area was produced. The suggested audio directional algorithm had the capacity of instantaneously gyrating the video camera to the assessed position of the blind areas. The challenge was to ensure that the production of the recommended algorithm had been higher than the quality index that was required [1, 19].

Audio Directional Source Algorithm Founded on the Application of the Least Squares Approach

Notwithstanding, the suggested audio direction positioning algorithm that was demonstrated in the previous section may not operate effectively in actual surroundings. This is attributed to the diverse noises and the interference that was derived from the voices of the empiricists. Consequently, an algorithm which has been designated ADS’ had been produced by applying the least squares approach in order to enhance the precision of the position estimation of the original ADS algorithm [1, 19].

Initially, the pairs of the options were manifested by applying the following formulae:

LOC Audio Source = Δ θ(x, y) + Δ θ MIC (X)

In the event that Δ θ MIC (X) < Δ θ MIC (Y)

LOC Audio Source = Δ θ MIC (X, Y) – Δ θ MIC (X)

In the case that Δ θ MIC (X) > Δ θ MIC (Y)

The estimation of the positioning was ascertained by the implementation of the cardinal coordinates of the real positions of the audio sources. This quality was manifested as (x1, y1), (x2, y2), (x3, y3)…. (xn, yn). The approximated positions of the audio source origins were linearly estimated by the application of the following formula:

a1 x + a0 = y

The formula was applied by the consideration of x1 and y1 being variables which are completely autonomous and dependent. The constants which had been required to be discovered were a0 and a1 [1, 19].  There may have been uncertainties in the calculations between x1 and y1. The uncertainties (e) were expressed by the formula:

y1 – a0 – a1x1 = e

The application of the least squares approach is one of the most frequently applied methods for approximating equations of the first order. The least squares method is frequently applied for the calibration algorithms in a number of disciplines. The least square methods were adapted to the audio directional positioning calibration algorithm. The aggregate of the squares of the uncertainties can be manifested as:

Σni = 1ei2Σni = 1(y1 –a0– a1x1)2 = S

In order to decrease the squares of the least sum squares of the errors (S) for a1 and a0, the derivatives of S in relation to a1 and a0 are established at 0:

0 = -2 Σni = 1(y1 –a0– a1x1) = δS/ δa0

0 = -2 Σni = 1(y1 –a0– a1x1) x1 = δS/ δa1

The equations for the least sum squares of the uncertainties can be simplified in order to derive the following equations:

Σni = 1 y1Σni = 1 a0 – a1 Σni = 1 x1 = 0

Σni = 1 x1y1– a0Σn i = 1 x1 – a1 Σni = 1 x21 = 0

In the consideration of Σni = 1a0 0 = na0, the equations may be demonstrated in the following simplified forms:

Σni = 1y 1 = na0 + a1 (Σni = 1 x1)

Σni = 1 x1y 1 = a0 (Σni = 1 x1) + a1(Σni = 1x21)

nΣni = 1 x1y 1Σni = 1 x1 Σni = 1 y 1 (n Σni = 1 x21– (Σni = 1 x1)-2)

In the insertion of the values of a0 and a1 into the following equation, the corrected localization outcome is derived from the following:

y1 – a0 – a1x1 = e [1, 19]

Methodology

The experiment was conducted with the audio directional finding of the sound sources matrix in the Gateway Center Union Station subterranean parking lot in Los Angeles, California and the Avalon Oak Creek underground garage in Los Angeles, California. In order to provide a testing ground for the recommended formulae, there had been the production of a specialized software element which is founded on the Atmega 128 MCU which was designated as the audio source receiving and analyzing module. The production examinations of the recommended model were carried out in the Gateway Center Union Station underground garage and the Avalon Oak Creek underground garage. The audio directional receiving and analysing module was established on a tripod [1, 19].

In these surroundings, the sound directions were set at 45 °, 90 °, 135 °, 180 °, 225 °, 270 °, 315 ° and 360°. These audio signals where conveyed and collected by the module, which was able to realize the algorithms. Consequently, the information was delivered to the monitoring system by means of the internet. There were twenty trials conducted for each angle. The measurements were documented and placed on Table 2 [1, 19].

Results

Table 2: Estimated Location with Respect to Angle

45° 90° 135° 180° 225° 270° 315° 360°
Trial 1 40 70 130 150 220 275 300 330
Trial 2 25 85 120 210 205 270 300 390
Trial 3 27 55 125 195 207 230 340 375
Trial 4 50 70 150 185 230 250 300 370
Trial 5 20 90 120 210 205 270 315 330
Trial 6 40 70 140 160 220 250 315 340
Trial 7 120 55 230 180 300 230 295 360
Trial 8 110 90 210 160 290 270 300 380
Trial 9 10 70 120 160 190 250 310 370
Trial 10 20 90 130 180 200 270 310 390
Trial 11 25 110 135 200 205 190 310 390
Trial 12 25 90 135 190 205 270 340 340
Trial 13 25 40 135 210 205 210 300 360
Trial 14 40 110 140 210 220 290 305 390
Trial 15 30 90 135 190 210 270 300 370
Trial 16 40 70 140 160 220 250 307 340
Trial 17 25 70 130 165 200 250 300 345
Trial 18 20 90 135 200 205 270 330 380
Trial 19 30 90 140 215 190 270 310 395
Trial 20 10 75 120 160 195 255 305 340

Analysis of Results

In this segment of the results section, the production of the suggested audio directional positioning algorithm is applied in addition to the audio directional source calibration algorithm. These calculations are founded upon the least squares method (LSM).  The ADS and the ADS’ algorithm can be applied to the determination of the audial sources in underground apartment building parking areas in addition to a variety of other applications based on its cost avoidant quality. The audio directions that were applied were 45°, 90 °, 135°, 180°, 225°, 270°, 315° and 360° [1, 19]. The values of 360 ° degrees and values that were more elevated than 360° were evaluated as values greater than 360° for the purpose of being able to average without the application of negative values.

The assessed location measurements angles of the LOC audio direction and the ASDLSM’ in the orientation of 45° are demonstrated in Table 2 and Figure 2. The maximum uncertainty of the LOC audio Source  + ASD’LSM is assessed as 75° and 5° in the first and the eighth trial when evaluated in comparison with the terrain verified angle of 45°  among the twenty trials. This infers that the uncertainty of the evaluated angle decreased by LOC Audio Source + ASD’LSM was 70.58 ° or 92.9%. This implies that the maximum error was reduced from ± 75° to ± 4.42° [1, 19].

The assessed location measurements angles of the LOC audio direction and the ASDLSM’ in the orientation of 90° are demonstrated in Table 2 and Figure 3. The maximum uncertainty of the LOC audio Source  + ASD’LSM are assessed as 50° and 5.2° in the second and the third trials when evaluated in comparison with the terrain verified angle of 90°  among the twenty trials. This infers that the uncertainty of the evaluated angle decreased by LOC Audio Source + ASD’LSM was 45.75 ° or 93.1%. This implies that the maximum error was reduced from ± 75° to ± 4.42° [1, 19].

The assessed location measurements angles of the LOC audio direction and the ASDLSM’ in the orientation of 135° are demonstrated in Table 2 and Figure 4. The maximum uncertainty of the LOC audio Source  + ASD’LSM are assessed as 91° and 5.4° in the first  and the seventh trials when evaluated in comparison with the terrain verified angle of 135°  among the twenty trials. This infers that the uncertainty of the evaluated angle decreased by LOC Audio Source + ASD’LSM was 85.6 ° or 93.3%. This implies that the maximum error was reduced from ± 81.2° to ± 5.4° [1, 19].

The assessed location measurements angles of the LOC audio direction and the ASDLSM’ in the orientation of 180° are demonstrated in Table 2 and Figure 5.  The maximum uncertainty of the LOC audio Source  + ASD’LSM are assessed as 35.0° and 8.3° in the second  and the fourth trials when evaluated in comparison with the terrain verified angle of 180°  among the twenty trials. This infers that the uncertainty of the evaluated angle decreased by LOC Audio Source + ASD’LSM was 26.7 ° or 76.3%. This implies that the maximum error was reduced from ± 35° to ± 8.3° [1, 19].

The assessed location measurements angles of the LOC audio direction and the ASDLSM’ in the orientation of 225° are demonstrated in Table 2 and Figure 6.  The maximum uncertainty of the LOC audio Source  + ASD’LSM are assessed as 75° and 5.5 in the first  and the seventh trials when evaluated in comparison with the terrain verified angle of 225°  among the twenty trials. This infers that the uncertainty of the evaluated angle decreased by LOC Audio Source + ASD’LSM was 70.725 ° or 92.7%. This implies that the maximum error was reduced from ± 75° to ± 4.275° [1, 19].

The assessed location measurements angles of the LOC audio direction and the ASDLSM’ in the orientation of 270° are demonstrated in Table 2 and Figure 7.  The maximum uncertainty of the LOC audio Source  + ASD’LSM are assessed as 55° and 4.7 in the first  and the eleventh trials when evaluated in comparison with the terrain verified angle of 270°  among the twenty Trials. This infers that the uncertainty of the evaluated angle decreased by LOC Audio Source + ASD’LSM was 50.325 ° or 91.5%. This implies that the maximum error was reduced from ± 55° to ± 4.675° [1, 19].

The assessed location measurements angles of the LOC audio direction and the ASDLSM’ in the orientation of 315° are demonstrated in Table 2 and Figure 8.  The maximum uncertainty of the LOC audio Source  + ASD’LSM are assessed as 25° and 5.8 in the first  and the ninth trials when evaluated in comparison with the terrain verified angle of 315°  among the twenty tests. This infers that the uncertainty of the evaluated angle decreased by LOC Audio Source + ASD’LSM was 85.6 ° or 75.34%. This implies that the maximum error was reduced from ± 25° to ± 1.475° [1, 19].

The assessed location measurements angles of the LOC audio direction and the ASDLSM’ in the orientation of 360° are demonstrated in Table 2 and Figure 9. The maximum uncertainty of the LOC audio Source  + ASD’LSM are assessed as 35° and 5.1 in the second  and the fourth trials when evaluated in comparison with the terrain verified angle of 135°  among the twenty tests. This infers that the uncertainty of the evaluated angle decreased by LOC Audio Source + ASD’LSM was 85.6 ° or 84.1%. This implies that the maximum error was reduced from ± 35° to ± 2.925° [1, 19].

Table 2: Reduction in Error Resulting from the Application of the ADS’ Algorithm

Angle of the Audio Sources  

45°

 

90°

 

135°

 

180°

 

225°

 

270°

 

315°

 

360°

 

Mean (°)

Maximum Uncertainty of LOC Audio Sources (±) 70.58 75 81.2 35 75 55 23.525 32.035 55.9175
Uncertainty LOC Audio Source  + ASD’LSM (±) 5 5.2 5.4 8.3 5.5 2.7 5.8 5.1 5.375
Uncertainty Reduction Index 92.9% 93.1% 93.3% 76.3% 92.7% 95.1% 75.34% 84.1% 90.4%

Chapter 4- Discussion

Audio Directional Positioning Founded on the Audio Origin Amplitude

In the initial circumstance of the automobiles entering or egressing sequentially on one of the underground avenues of the underground parking areas. In this situation, the determination of the audio origin and the gyration of the video surveillance systems can be effectively performed. In the second circumstance of the automobiles moving synchronously on the multipole underground avenues. The ascertaining of the audio origin and the gyration of the video surveillance system to the targeted automobiles cannot be readily accomplished. It is not feasible to determine all of the audio sources of the vehicles which are in motion on the multiple avenues of the underground parking area on schedule [14].

This is attributed to the premise that the multiple automobiles that are in motion would have the requisite of the discernment of the multiple audio origins, filtration of the noise, evaluation and supplemental computing capacities. As a result, the operational surroundings for the recommended formula in the circumstance  of the intersection of the avenues in the underground parking areas is considered to be valid for the initial circumstance, where the automobiles are in a sequential motion, but not the second circumstance where the automobiles are moving at the same time. Notwithstanding, the recommended formula can be modified for the second circumstance  in the event that the automobiles were to move sequentially  on the various avenues as a causal attribute of the minimal traffic in the underground parking areas [15].

Over the past decade, sensor based network underground parking lot administration systems have been the subject of attention on behalf of researchers. A parking lot application has been proposed which applied ultrasonic sensors in order to tabulate the number of vehicles which had ingresses and egressed [16]. Ultrasound has also been recommended in order to ascertain the quantity of vacant spaces in a parking establishment. Notwithstanding that these systems have modified the production of the underground parking areas by means of the applications of sensors, the security challenges in the underground parking areas have been the most neglected. As the quantity of underground parking areas increases, the volume of crimes which are committed increases as well. The consensus of the underground parking areas have not adequately addressed these challenges. The security in the underground parking areas is usually conducted by automobile anti-theft devices or video surveillance cameras which are implemented in the underground parking areas. These approaches have restrictions with regards to emergency situations. In the video bases camera, there should be security personnel who are monitoring the camera in real time. As a result of the pragmatic limitation of continuously monitoring the security systems, the video based surveillance systems are limited in their responses. In addition, the majority of the video cameras have blind spots. This infers that these are areas to which the camera’s vision has no access. This is where the audio directional findings system is most effective [9, 10].

The range that a normal acoustic origin localization matrix is limited by the qualities of the delivery range of the radio signals and the audio sources. In the event that the sound must travel a greater distance than the radio signal that corresponds, there may be a miscalculation on the localization systems due to the challenges in collaborative information sharing between the sensor nodes. The recommendations for the resolution of this challenges are the MBFM (Muzzled Blast Fusion Method) and the AML (Approximate- Maximum Likelihood) have been suggested for the resolution of this challenge. The IEEE802.11 network is applied by the AML systems which has a more expansive range than IEEE802.15 [13].

The muzzled blast fusion method is a systems which when it encounters an audio source conveys all of the data to a base station. The localization matrix is outfitted with mutual sensor nodes is limited in its ability of providing overage. This is attributed to the restricted production of the nodes. In the event that one of the audio origins is spread in the multi hop range, the quantity of nodes that can detect the audio sources is enhanced. The networks propagation causes the networks to collaborate on the detection information that is derived from the nodes. The nodes which are resource limited do not have the capacity of administrating the increase. Consequently, the packet losses are enhanced. The packet loss may cause uncertainty in the systems. There are many instances where the dimensions of the collaborated data is to expansive for the nodes to administrate. Consequently, a system which applies cost avoidant nodes usually has the ability of sensing in a restricted area where the communication takes place in a singular hop method [13].

The multiple hop communication which is designated as MDSL (multi- hop distribution source localization). The process has the quality of localizing the various acoustic origins which can be found in an extensive area with cost avoidant sensor nodes and does not have the requisite of supplementary hardware or an administrative communication model. In this formula, each of the nodes collaborates with regards to the detected data within the range of one hop. The leading node which is chosen within the range of one hop has the function of conveying the information to the base node. The base node has the capacity of categorizing the imprecise outcomes and derive a precise position of the audio source [13].

The matrix decreases the network overhead and has the ability of detecting a multitude of acoustic origins that are found in the range of the multi- hop. The eradication of imprecisions can be applied in order to differentiate the nodes which are directed toward audio detection. The formula is applied in order to function with the conventional networks nodes which apply cost avoidant audio sensors which require no supplementary hardware [13, 17].

The DSL monitors a solitary acoustic origin by means of the low production sensing nodes. In any category of audio source, the sensing nodes collaborate on the detection intervals and the source position is ascertained by the acoustic sensor that encountered the audio source first. The DSL is able to carry out its tasks when the audio source is dispersed over a wide range. This is attributed to the collaboration of the detection data by means of the nodes. This collaboration by the DSL system is a challenge in the multi – hop systems. Another challenge is the efficient eradication of the imprecise outcomes which are the results of the quality of the audio sources and the radio signals. In the event that the variety of audio sources are synchronously developed, it becomes more challenging to find effectively find the audio sources [13, 17].

In order to remedy the deficiencies of these approaches, a new algorithm has been introduced in this research that operates with cost avoidant sensing nodes. This new algorithm encounters the varied audio sources that take place in an amplified region. The leader selection take place with the node that has the first detection of the audio source. The leader formulates an essential event region (EER) which is self -centered. The EER has the qualities of being a simplified circular education of the Voronoi region for the resource limited components. The diameter of the Voronoi area is demonstrated by the following formula:

Σ│pl – pi│ (h- 1) – 1 α = W, i = 1, 2, 3… h where i ≠ l

In this case pl and pi are the positions where the leader node is located, including the ith node in the collection [13].

The quantity of nodes is represented by h, which serves as the coefficient for the environmental limitations. The value of α is manually determined by the user in accordance with the surroundings. In the event that the user does not apply a value to α, the value is established by default at one. The leading node creates the Voting Grid by conducting the division of the EER with regards to a value that is equal to the predetermined size. This value is communicated to the other components of the groups [11, 13].

In the distributive processing: Each of the nodes individual assesses the audio source position by a comparison of its location and tie of detection to the other group components. In each of the analysis, a separating line is delineated a having a quality that is perpendicular to the middle intersecting point of the line that is found between the two nodes. The source is normally positioned on the lateral aspect of the node which manifests a previous detection time. Consequently, the right façade grids of the manifested Voting grid receive a point. Subsequent to the evaluation, the outcome of the vote is conveyed to the leader node [11, 13].

In the validation the leading node adds the voting outcomes from the other components in the group and validates the conclusive source position. Initially, the nodes which are labeled A, B, C, D, and E discover an audio source. The position of the audio source is mutually conveyed. The final node, E which had discovered the source at an interval of 3 mms was chosen as the leading node. The Node E formulates a Voting Grid that is founded on the EER and communicates this quality to the other nodes. All of the nodes which are on the Voting Grid votes by evaluating the detection period differential and the distance that is manifested from other nodes. The voting outcomes are conveyed to the Node E, which adds the outcomes from the other nodes. The grid which has been the recipient of the most elevated score 37 is approximated as the position of the audio source [11, 13].

DSL had been assessed in a limited size surrounding, where each of the nodes were positioned within the range of one- hop. The nodes that were not within the one- hop range did not have the capacity of being recipients of the data. The leading node is chosen in each of the one – hop zones where the audio sources had been detected. There may be several leading notes which are created as the audio detection may take place in more than one zone. As a result, the localization had the potential of being misconducted by the leading nodes with respect to the solitary audio source. Notwithstanding, this circumstance is not always the case when an audio source is dispersed over the range of the multi- hop. Eventually, there is one solitary node which is selected as the leading node [13].

There are two circumstances where the leader is misevaluated. One of the situations takes place when the audio sound is discovered by more than one of the groups and the distance which separates the nodes is too vast in order to effectively communicate. The other circumstance takes place when the orientation of the node is distinct from the orientation of the data communication [13].                                                                                                                                                                                                                                                                                                    Accuracy of the System

Upon the activation of an automobile alarm, the location is reviewed with regards to the camera. As the vehicle whose alarm had been activated is demonstrated in the video picture, the attempt was regarded as being successful. Notwithstanding, that the calculated outcome that was derived from the sensor precisely demonstrated the zone in which the alarm had been activated. Notwithstanding, the video camera frequently cannot demonstrate the zone due to the diverse obstacles or the restriction in the field of view of the camera. In order to mitigate this circumstance, the cameras were placed symmetrically and the two cameras which had been in the proximity of the zone of the audio event had been applied in the demonstration [1].

In the application of the server by means of the laptop, the result was manifested in the diverse regions of the underground parking garages. There had been three USB microphone router nodes which had been applied as routers. In the application of the multiple hop methods of communicating, the results of any part of the experimental areas that were applied in the Gateway Center Union Station underground parking area and the Avalon Oak Creek apartment complex underground parking area [13]. The ratio of success was approximately 90 – 94%. This type of outcome is reasonably precise in actual environments.

The production comparison that was manifested between the recommended audio directional finding algorithm  (LOC Audio sources ) and the positioning of the audio finding algorithm in collaboration with the audio directional finding compensation algorithm  (ADS’)  which was founded on the calibration algorithm based on the LSM  (LOC Audio Sources  + ADS’) in the directions of 45°,90 °, 135 °, 180°, 225°, 270°, 315° and 360°as demonstrated by the Figure 2- 9 are summarized in Table 2. The maximum uncertainty of the audio sources has been diminished by 88% in the LOC Audio sources + ADS’. The enhancement is outstanding and it is anticipated that the (LOC Audio Sources  + ADS’) will; be able to perform an integral participation in the audio directional fining of the audio sources for the underground parking areas as an attributed of the production but the extendibility and the simplicity of the formula [1, 19].

Furthermore, the production in the context of the decreasing indexes and the approximated angles of the audio sources in the directions of 45°, 90 °, 135 °, 180°, 225°, 270°, 315° and 360°are demonstrated in Table 2 and figure 210. The approximated average of the decreased index of uncertainty is    in comparison with the LOC Audio Sources. It has been demonstrated that the production of the LOC Audio Sources + ADS’ can be adequately modified not exclusively for the audio directional finding of the module for the underground parking areas [1, 19]. The module can be adapted to the diverse audio source applications due to its cost avoidant qualities. Additional field tests are required in order to ensure the production of the commercially marketed products in the future.

The results that have been demonstrated are the outcomes of the sequential entering and egressing traffic of the automobiles on the underground parking areas avenues and at the intersections. The production of the recommended algorithm in the operation environment on the intersection of a number of viaducts is an area for future research [1, 19].  In order to more completely evaluate the production of the recommended audio directional finings algorithm in underground parking areas by comparing it with the related research, the requisite of the assessment of the performance of the audio directional finding algorithm and the audio directional finding calibration algorithm will require additional research in distinct environments. Consequently, additional research in distinct environments is required. It is implied that the majority of the theoretical formulas in the related research are complicated and ineffective for modifications in the underground parking areas. Consequently, an evaluation and comparison of the recommended formula with other formulas with identical localization parameters has not been demonstrated in this research.

Conclusion

The contributions that are made in this dissertation are three fold. Initially, the audio directional finding is an efficient algorithm which facilitates the endeavor to be conducted in an expansive area that the conventional matrices cannot cover as a result of the distinct ranges of the audio sources and signals. In addition, the   module has the capacity of being able to detect numerous audio sources that are synchronously produced. This characteristic enhances the application of the algorithm in consideration that multiple sources are frequently applied in actual environments. Furthermore, the algorithm is not complicated and can be implemented in cost avoidant sensing nodes. This is a very practical feature which enables the overage of an area with cost avoidant sensing nodes. The audio directional finding calibration algorithm had been presented in this research. This system had been modified in order to function effectively in the environment of underground parking areas. The performance of the audio directional algorithm, in addition to the audio directional fining calibration algorithm have been effectively applied in this research for a variety of directions. The recommended positioning of the acoustic sources was performed in the underground parking areas of the apartment complexes and office buildings. The algorithms have the capacity of being able to discern the audio sources which are the result of the automobiles entering and egressing the underground parking area. In addition, the other sounds which must be discerned are the sounds of car alarms, slamming of car doors and dialogues between individuals. These sounds are relayed to the module which directs the video surveillance camera the angle to which it should be gyrated in order to direct additional attention. The mean uncertainty reduction rate of the audio directional finding of the LOC Audio Sources + ADS’ over the LOC Audio Sources is equivalent to 90.4%. The audio directional fining and the audio directional finding calibration algorithm can be adequately adapted for the audio directional encountering of sources in the underground parking areas. This characteristic has been demonstrated in the research. The audio directional fining and the audio directional finding algorithm can be applied for the diverse sound origins as a result of its cost avoidant characteristic. Future research would include testing the audio directional finding algorithm in distinct surroundings. It is strongly anticipated that the recommended algorithms can be applied with any video surveillance monitoring systems in the supervision of the security activities for the underground parking areas. The suggested algorithms contribute to the distribution of the audio directional finding technology in the fields of home security and safety.

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