Abstract:
An acoustic detection and localization system includes a signal acquisition stage, a biomimetic processor, and an acoustic feature processor. The system uses multiple acoustic cues including spectral content, inter-aural time delay (ITD), inter-aural intensity difference (IID), and periodicity content to detect, classify, and localize sound sources. Pairs of acoustic sensors are arranged geometrically with a spacing depending on the application. The biomimetic processing provides for echo suppression to enhance performance in reverberant environments, and automatic gain controls for robustness in noisy environments. Applications include mobile robotic platforms for reconnaissance and surveillance, helmet mounted systems for sniper detection, vehicle-mounted systems for combat awareness, general civilian security systems, and systems for environmental monitoring and tracking of animals.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
   This application claims the benefit under 35 U.S.C. § 119(e) of U.S. provisional patent application 60/676,189 filed Apr. 29, 2005, the disclosure of which is hereby incorporated by reference in its entirety. 

   STATEMENT OF GOVERNMENT RIGHTS 
   The invention disclosed in this application was made with Government support under Contract Number N00014-00-C-0314 awarded by the Department of the Navy, and Contract Number DAAD19-00-2-0004 awarded by the U.S. Army Research Office. The Government has certain rights in the invention. 

   BACKGROUND 
   The invention pertains to the field of acoustic signal processing with regard to sound source classification and localization. 
   SUMMARY 
   In accordance with the present invention, apparatus and methods are disclosed for acoustic signal processing for sound source classification and localization using “biomimetic” techniques, i.e., techniques that are modeled after structures and functions found in biological systems such as mammalian aural physiology. The system uses several acoustic cues including Interaural Time Delay (ITD), Interaural Intensity Difference (IID), spectral cues, as well as periodicity cues to localize and identify sound sources. Three primary stages of processing include an acoustic acquisition stage, a biomimetic processing stage, and a feature processing stage. The invention maybe utilized with multiple sensor configurations, in fixed positions, on mobile platforms, or worn as part of standard equipment e.g. helmet based arrays. 
   Illustrative embodiments may be inexpensive to operate and maintain in the field and require relatively little power in use as a passive listening device. The system may be modular and easily configurable by changing or swapping modules. System sensor configurations can easily be adapted for a multiplicity of applications with a varying number of sensors and sensor geometries. Sensor configurations can be on very small scales, as small as a one-inch platform, or can be used on larger scale platforms with small changes to software or hardware. The system can be used with a single-user stand alone system or as a part of a distributed network. Performance is improved for specific mission applications with the addition of acoustic environmental and sound source models to the feature processing stage. A key feature of the system is the ability to operate in reverberant and noisy environments through the use of biomimetic processing. Single sensor arrays can be used for multiple tasks with the addition of new feature processing stages simultaneous using the same acoustic acquisition and biomimetic processing stages. Multiple specific application areas are identified. 
   The system may utilize a mixture of analog and digital devices with readily available off-the-shelf components. The system may be used with readily available power supplies and easily integrated with a variety of existing technologies and platforms. The feature processing for each sensor array unit provides users with low bandwidth information to network with other sensor arrays or to drive devices or can be used as part of heads-up displays as well as providing data logging capabilities for later processing and analysis. 
   The system provides accurate locations for sound sources and very robust cues for classifying sound sources. The modularity and mission-configurable feature processing in conjunction with the reverberation and noise tolerance of the system make it a low cost, robust, and versatile option for many acoustic processing tasks. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The foregoing and other objects, features and advantages of the invention will be apparent from the following description of particular embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. 
       FIG. 1  is a block diagram of a biomimetic acoustic processing system in accordance with the present invention; 
       FIG. 2  is a schematic depiction of a sensor configuration usable in the system of  FIG. 1  and illustrating the effect of differences in the paths from a sound source to the sensors; 
       FIGS. 3-5  are schematic depictions of alternative sensor configurations usable in the system of  FIG. 1 ; 
       FIG. 6  is a block diagram of a biomimetic processing sub-system in the system of  FIG. 1 ; 
       FIG. 7  is a block diagram of a biomimetic preprocessor in the biomimetic processing sub-system of  FIG. 6 ; 
       FIG. 8  is a block diagram of an automatic gain control circuit in the biomimetic preprocessor of  FIG. 7 ; 
       FIG. 9  is a block diagram of spike generator circuits in the biomimetic preprocessor of  FIG. 7 ; 
       FIG. 10  is a schematic depiction of inter-aural time delay (ITD) processing in the system of  FIG. 1 ; 
       FIG. 11  is a block diagram of an ITD processor in the biomimetic processing sub-system of  FIG. 6 ; 
       FIG. 12  is a block diagram of an inter-aural intensity difference (IID) processor in the biomimetic processing sub-system of  FIG. 6 ; 
       FIG. 13  is a schematic diagram depicting periodicity processing in the system of  FIG. 1 ; 
       FIG. 14  is a block diagram of a periodicity processor in the biomimetic processing sub-system of  FIG. 6 ; 
       FIG. 15  is a schematic diagram of an implementation of a spike generator circuit such as shown in block form in  FIG. 9 ; 
       FIG. 16  is a schematic diagram of an implementation of an automatic gain control circuit such as shown in block form in  FIG. 8 ; and 
       FIGS. 17 and 18  are perspective views of an embodiment of an enclosure for the system of  FIG. 1  for a mounted mobile application such as on a mobile robot. 
   

   DETAILED DESCRIPTION 
     FIG. 1  shows a processing system using pairs of acoustic sensors to extract features and process them for specific tasks. The system itself has a parallelized processing scheme with three primary processing stages. The first stage is an acoustic acquisition system  10  which includes (i) a sensor array receiving sound from a source  12  and having a geometric sensor arrangement to optimize acoustic features for a given task, and (ii) basic signal conditioning to prepare the raw acoustic waveforms from the sensors as input to the hardware system. The second stage is a biomimetic processing sub-system (biomimetic processing hardware)  14  which includes a biomimetic preprocessor, a set of feature extractors which use pair-wise sensor inputs for inter-sensor time delay (ITD) and inter-sensor intensity difference (IID), as well as individual sensor periodicity processors (all described in more detail below). A set of feature processing algorithms  16  (also referred to as “feature binding”) performs feature processing to estimate sound source position and to perform sound source classification. The feature processing algorithms  16  may include an application-specific decision matrix which utilizes the extracted position and classification information to drive external devices and display subsets of the analyzed data to system users, depending on the particular application of the biomimetic processing system. 
   The biomimetic approach uses mammalian physiology as a model for processing and extracting acoustic features. The processing involved in mammalian auditory systems is based on two ears or pairs of sensors. In the disclosed technique, the processing method employs pairs of sensors to acquire acoustic information for localization. The arrangement and geometry of the sensors is an integral aspect of acoustic acquisition and determines what acoustic features are available for extraction. The particulars of the sensor geometry are utilized within the feature processing algorithms  16  to classify and group features as part of “scene analysis”, a task involving both identification and localization of sound sources. 
   With reference to  FIG. 2  as an example, two sensors  18 - 1  and  18 - 2  may be placed some distance d apart creating a variable path length to the sensors  18  from any given sound source  12  based on its position in space. Assuming that the distance D to the source  12  is much greater than the separation d, the sound source  12  at any given position will have a difference in path length to the sensors  18  defined as d′=Path  1 −Path  2 . The angle from which the sound source emanates relative to the axis of the sensors  18 , referred to as E, is equal to the arc-cosine of d′/d. The quantity d′ can also be thought of in terms of time of arrival of the sound at the sensors, often referred to as ITD (inter-aural time delay). The relationship of ITD to d′ is defined as ITD=d′/(Speed of sound). 
   Given any two-sensor arrangement in a two dimensional plane, there are two values of 0 which produce the same ITD value, and thus for a given ITD value there is an ambiguity in position. This ambiguity is often referred to as ‘front-back’ ambiguity. One pair of sensors can therefore only provide a localization estimate in one planar dimension, for example azimuth, and that estimate will have a reflected ambiguity about the axis of the sensors. This situation is illustrated in  FIG. 3(   a ). In the plane of the sensors, a V-shaped pair of lines defines points of equal ITD. If the estimates are extended to a three dimensional space, the V is rotated about the axis of the sensors to become a conical surface of points that all produce the same ITD. This surface is referred to as a ‘cone of confusion’ and is illustrated in  FIG. 3(   b ). 
   To correct these dimensionally limited estimates and ambiguities, different numbers of sensors and different sensor geometries can be used. Although a biological system (e.g. an animal) may be limited to a pair of sensors, there is a biological strategy for obtaining an unambiguous estimate of sound source position in three dimensional space. Typically when trying to localize a sound, animals will change their head position several times. The position changes can be thought of as additional sensor pairs used to reduce the ambiguity in sound source location. Thus in the disclosed technique, additional pairs of sensors can be included as additional instantaneous ‘looks’ at the sound source. The initial geometry using a single pair of sensors  18  can extended to multiple sensor pairs. Orientation and arrangement of the additional sensors pairs is important to maximize new acoustic information. 
     FIG. 4  illustrates a particular example of employing more sensors to reduce or remove ambiguity. As shown in  FIG. 4(   a ), a single pair of sensors  18 - 1  and  18 - 2  produces an estimate in two dimensions with a front/back ambiguity in the direction orthogonal to the sensor axis, an ambiguity that extends to a cone in three dimensional space. Using three sensors  18  as shown in  FIG. 4(   b ), the addition of a third non-collinear sensor  18 - 3  along the axis orthogonal to the original sensors  18 - 1  and  18 - 2  removes the front/back two-dimensional ambiguity, although there is still an ambiguity in three dimensional space. The addition of a single extra sensor such as sensor  18 - 3  adds two additional pairs with which to make estimates—pairs ( 18 - 1 ,  18 - 3 ) and ( 18 - 2 ,  18 - 3 )—in addition to the original pair ( 18 - 1 ,  18 - 2 ). 
     FIG. 5  illustrates additional examples of using additional sensors. A fourth sensor  18 - 4  may be added in-plane in the manner shown in  FIG. 5(   a ), for example. Such an arrangement does not resolve the three dimensional ambiguity of the arrangement of  FIG. 4(   b ), but given certain assumptions about the application will completely resolve the available positions in space. An in-plane sensor set such as in  FIG. 5(   a ) is ideal for applications where low profiles are necessary, such as flush-mounted wall sensors or mountings that require aerodynamic design. For many flush-mounted applications, the acoustic space of interest is limited to one hemisphere (e.g., the hemisphere to one side of a wall), and therefore the application itself removes potential three-dimensional ambiguity. Four in-plane sensors also has the advantage of fault tolerance—any one sensor can be destroyed and the remaining system will function the same. Also, in environments having poor signal-to-noise characteristics, the additional sensor pairs help to improve signal levels. Placement of sensors in a two dimensional space may have other issues apart from resolving front-back ambiguity. Orthogonal axes are the ideal, but that does not constrain sensor location entirely. Dependent upon available space and the processing method used, distance between sensors should ideally be identical for processing purposes. 
   As shown in  FIG. 5(   b ), one sensor  18 - 4  may be moved out of the plane of the other three, in which case the sensor set will have no ambiguity with any sound source position in three dimensional space. The out-of-plane sensor configuration requires that the sensor set occupy some volume as well as having all the constraints that exist from in-plane sensor sets. For many applications where out-of-plane sensor sets are required, a tetrahedral arrangement may be beneficial, as this arrangement can maximize sensor spacing given a certain volume, maintain equal spacing of all the sensors to maximize processing efficiency, and provide the desired orthogonal out-of-plane components. 
   When dealing with biomimetic feature processing, acquiring the source signal in a way that optimizes information in the features is very important for the function of the system. In the case of auditory processing, there are four basic features which include, but are not restricted to:
         1) Spectral content (the various frequencies that make up a signal) [monaural cue]   2) Interaural Time Delay or ITD [binaural cue]   3) Interaural Level/Intensity Difference (ILD/IID) as well as spectral notches [binaural cue]   4) Pitch/Periodicity cues [monaural cue]       

   All of these cues are useful for acoustic source identification and or localization, although the exact mechanisms by which these cues are extracted and utilized in biological systems are still a topic of great debate and are by no means obvious or well explained. Sound source localization and identification are not mutually exclusive, and in a biomimetic context, can be synergistic cues. Sound location helps to segregate sound sources which improves identification, and identifying a sound source also helps to distinguish it from others, which in turn can improve localization. 
   Typically, binaural cues are used primarily for localization, and monaural cues primarily for identification. Binaural cues require two receiving sensors, while monaural require only a single receiver. An important distinction in the biomimetic approach to processing is that it uses pair-wise sensor information when dealing with localization tasks. Arrangements with multiple sensors are taken by pairs and are put in a context of biological behaviors and methods used in biological systems to solve acoustic problems. 
   Given these features and a pair-wise arrangement of sensors, with a minimum number of sensors generally being both the most cost effective and desirable design, the methodology for designing a sensor geometry for any given application takes into account three major constraints: 
   1) Properties of the acoustic sound source 
   2) The acoustic environment in which the sound source is to be identified/localized 
   3) The form factor of the application, available size shape constraints 
   When considering an acoustic sound source there a several properties which are considered when designing a sensor geometry:
         1) The frequency content (affects overall size for good sensitivity)   2) The freedom of position of the sound source, i.e. a 2D space or 3D space or a confined sub-region of space (affects sensor placement to avoid ambiguities)   3) Is the sound source transient (e.g. gun shot) or persistent (e.g. vehicle) (affects pre-amplification and pre-filtering of acoustic signals)   4) The dynamic range of the signal, i.e. what range of signal levels can be expected in the application (affects the type of pre-amplification and pre-filtering of acoustic signals)       

   When considering the acoustic environment for operation there are also several factors which will affect geometry design, these include:
         1) Is the application based in air or water   2) Is the environment highly reverberant (e.g. urban center with many reflectors or shallow water)   3) Background noise (e.g. wind noise, animal vocalization, vehicle noise, weapon fire etc.)   4) Is the application mobile and will the environment cause motion related artifacts (e.g. sensors are mounted on a HumVee which will operate while moving)       

   One of the most stringent constraints on design is the size and form factor of the application. When possible, certain aspects of the application physical structure are arranged to enhance acoustic features these include:
         1) The shape of the base on which the sensors are mounted—platform shape can increase resolution of ITDs, ILDs, and spectral notches   2) The size of the application—for any given application a larger size will improve localization   3) The spatial resolution required, i.e. does the application need to have accuracy in three dimensions and does the available space to mount the sensors allow for 3D localization       

   Several example applications are described below with corresponding considerations for sensor placement. 
   It should be noted that in addition to the various sensors  18 , the acoustic acquisition system  10  of  FIG. 1  may also include analog pre-amplifiers as a form of pre-processing circuitry. It may be desirable to filter undesired acoustic signals such as wind noise and to limit the dynamic range of the signal in a manner that does not cause severe spectral splatter. Pre-amplifiers using soft saturating diode circuits can be utilized to add significant dynamic range to the acoustic sensor array while minimizing spectral splatter. 
     FIG. 6  shows an exemplary biomimetic processing sub-system  14 . Several components perform parallel processing of the acoustic sensor signals to extract localization and sound source identification features. The raw acoustic signals (acquisition sensor signals) are taken from the acquisition system  10  and used as input to the biomimetic processing sub-system  14 . The output of each sensor  18  is run through a dedicated biomimetic preprocessor  20  and the resulting signals are distributed to various feature extracting modules, which include an inter-sensor time delay (ITD) processor  22 , a periodicity processor  24 , and an inter-sensor intensity difference (IID) processor  26 . The ITD processor  22  and the IID processor  26  require pair-wise sensor inputs, while the periodicity processor  24  works on the individual sensor signals. In the illustrated embodiment, all three processors  22 ,  24  and  26  are utilized, but alternative embodiments may employ any one or any pair of such processors as dictated by system requirements. The outputs of the processors  22 ,  24  and  26  are provided to a frame-based feature integrator  28  which generates a “feature stream”, i.e., a stream of higher-level features that have been identified by the biomimetic processing. 
     FIG. 7  shows the biomimetic preprocessor  20 , which takes in the raw acoustic signal from an individual sensor  18  and generates an array of outputs which are specialized conditioned signals representing temporal and spectral components of the acoustic signal. Each sensor  18  has one dedicated preprocessor  20  which generates a stream of pulsatile events be referred to as ‘spikes’, reminiscent of the biological action potentials generated by nerve fibers. Each preprocessor  20  as a unit is intended to replicate in a simplified form the essential processing performed by mammalian cochlea preconditioning the acoustic signals for other physiological based processing algorithms. 
   The first component of the preprocessor  20  is a filter bank  30  with characteristics designed to roughly match frequency properties of individual auditory nerves seen in mammalian physiology. The filter bank  30  consists of an array of bandpass filters each having a different center frequency (CF). The CFs of the bandpass filters are distributed logarithmically over the application specific frequency range. In illustrative embodiments, there may be between 16 and 64 bandpass filters. Each filter has a fixed bandwidth to center frequency ratio (referred to as Q 3 dB) preferably in the range of 3 to 10. The filter implementation can vary dependent upon space and power constraints. Typical implementations use resonant second order filters in conjunction with a high pass filter set approximately a quarter of an octave below the CF. An alternative design uses a cascade of second order filters which difference adjacent filter outputs to create bandpass filters with auditory like properties. 
   Each bandpass filter in the filter bank  30  generates an output stream that is referred to as a ‘frequency channel’ representing an instantaneous estimate of spectral content in an acoustic signal over the corresponding bandwidth. The filter implementations are not particularly crucial to the design; the important components of the design are the distribution of CFs and the approximate Q of the filters in order to produce frequency channels with proper characteristics to represent the spectral content of the acoustic signal. For localization, a relatively low Q in the range of 3 to 6 is good. The overall bandwidth of the set of filters depends on the application. For sounds such as mortar fire that are concentrated in lower frequency bands, the filters may span the range of 100 Hz to 1 kHz, whereas for higher-frequency sounds such as gunshots, a band from 100 Hz to 8 kHz may be desirable. The parallel structure of frequency channels is the basis of all of the processing that occurs in the biomimetic processing sub-system  14 , and the frequency channel dimension is maintained and represented in all the following stages of processing. 
   The frequency channel information from the bandpass filter bank  30  is passed into compression and rectification electronics  32  maintaining the parallel frequency channels from the filter bank. In an exemplary embodiment, the compression and rectification electronics  32  are identical for all of the frequency channels. The electronics consist of a log amplifier followed by soft rectifying circuit and low pass filter. The properties of the compression and rectification electronics are preferably designed to mimic hair cell transduction properties seen in the mammalian auditory system. The advantages of this processing stage are essentially two fold. First, the dynamic range over which the system can operate is increased, and second, the overall bandwidth of the signals being represented is decreased while maintaining a representation of the spectral content, even of high frequency signals. 
   Automatic gain control (AGC) circuitry  34  is an important part of the preprocessor  20 . The AGC circuits  34  take the compressed and rectified signals and perform an additional stage of signal conditioning to emphasize specific aspects of the amplitude information in each frequency channel, in particular the onset and offset of sound sources. For low frequency information such as low frequency pure tones and slowly varying envelope information for high frequency signals, the gain control circuits  34  tend to emphasize the rising edge of the acoustic waveform producing markers for the phase of the information, something akin to ‘glints’ in sonar signals. These circuits perform several roles including increasing system dynamic range, maintaining optimal operating points for spike generating circuits in loud ambient noise environments, and also a certain level of reverberation tolerance through short term signal adaptation. 
     FIG. 8  shows a block diagram of an AGC circuit within the AGC circuitry  34 . The AGC circuit is a highly nonlinear circuit which attempts to maintain a certain operating point over a wide range of signal levels. A cascade of AGCs are used to replicate the processing properties of neurological synapses using different operating points and different adaptation time constants. A first AGC  36  in the cascade has a fast adaptation time constant in its feedback loop, while a second AGC  38  has a slower adaptation time constant. The combination of fast and slow adaptation emphasize fast transients and signal onsets (e.g. on the order of 1 ms) while suppressing multiple transients and longer term variations that are often associated with reverberant environments (e.g. on the order of 30 ms or greater). The output of the AGC circuits  34  carries band-limited information from each frequency channel emphasizing transients and signal onsets or offsets as well as marking phase information for low frequency signals. 
   Referring back to  FIG. 7 , the output from the AGC circuitry  34  is used to drive spike generating circuits (electronics)  40 , which consist of three separate circuits per frequency channel, each of which generates a pulsatile stream of data. In an exemplary embodiment, the three circuits have nearly identical layout and construction with the exception of the voltage threshold at which they trigger. A low-threshold circuit triggers easiest and at the lowest sound levels; a medium-threshold circuit triggers at higher sound levels; and a high-threshold circuit triggers at the highest sound levels. Thus, each frequency channel is further divided into three sub-channels of different signal intensity ranges. The combination of these three circuits extends the dynamic range of the system and broadens the range over which critical sound features can be encoded, as explained in more detail below. 
     FIG. 9  shows a block diagram of the set of three spike generator circuits  40 -L,  40 -M, and  40 -H for a single frequency channel. Each spike generating circuit consists of a threshold circuit  42 , a threshold crossing detector  44 , an absolute refractory circuit  46 , and a relative refractory circuit  48 . The threshold circuit  42  performs a voltage comparison of the signal input to a reference value which is set relative to AGC operating points. The low threshold circuit  40 -L has a reference value approximately 20% above the quiescent AGC circuit output, which is slightly above ambient noise level; the medium threshold circuit  40 -M has a reference value set 10% above the maximum saturated steady state AGC output; and the high threshold circuit  40 -H has a reference value that is roughly 40% of the maximum peak AGC output. The combination of these three circuits extends the dynamic range of the system and broadens the range over which critical sound features can be encoded. The selection of reference values is important to the process to optimize not only the variation in information carried in each circuit, but also the quality of features encoded. Setting a reference value too close to the saturated AGC operating point will result in poor temporal feature extract, and setting a reference value too high relative to the peak AGC response will result in a circuit that will only respond to the onset of loud sounds in quiet environments. The above reference values have been shown through simulation to produce robust features over a broad range of acoustic conditions. 
   The refractory circuits  46  and  48  are used to regulate the threshold reference values as spikes are produced. Once a threshold circuit  40  has been activated, a narrow pulse of high voltage is generated and sent into a hardwired spike stream which can directly interface with digital hardware without an A/D interface. At the same time, the refractory circuits  46  and  48  are activated and drive up the reference values for the activated circuit. The absolute refractory circuit  46  temporarily sets threshold above maximum possible voltage levels in order to turn off spike generation. The relative refractory circuit  48  raises threshold levels to roughly 75% of the maximum voltage after the absolute refractory circuit  46  turns off, and then permits the threshold level to decay back to the initial reference value with a specific time constant on the order of 1-3 milliseconds. The refractory circuits  46  and  48  add a kind of hysteresis to the spike production process, reducing sensitivity to noise and ensuring good isolated production of spikes. The concept of generating spikes is taken from neurological systems and has a dual role in the system. First the spike generation is used as a method of marking ‘events’ in acoustic signals which can be anything from the onset of a sound to a particular phase of a low frequency signal. The second role is to convert high bandwidth information contained in the acoustic signals into low bandwidth signals that are easily interfaced with both digital processing and event based processing. The combination of the AGC cascade  36 ,  38  and the spike generation circuits  40  are critical for generating spikes which mark temporal features in the acoustics signals for biomimetic processing strategies. 
     FIG. 10  shows the general structure of biomimetic ITD processing. For ITD feature extraction, the spike streams generated by the preprocessor  20  for each sensor  18  are combined pair-wise, comparing spikes from the same frequency bands and thresholds across a pair of sensors  18 . Blocks  50 -A and  50 -B identify the respective sets of preprocessor output signals for two sensors A and B. The combining is depicted by the grouping of inputs to the ITD processor  22 , in which every signal pair for the two sensors A and B is routed to a counter circuit which looks for inter-spike timing intervals population by population. The large number of parallel pairs of inputs are simultaneously processed through the ITD processor  22  creating a sensor pair ITD feature stream. 
     FIG. 11  illustrates one frequency channel of the ITD processor  22  for a pair of sensor signals. For each of the three thresholds in each frequency channel, the ITD processor  22  includes a specialized counting circuit that generates an accurate estimate of the inter-sensor time delay of the sound source wave front based on the physical position in space of the sound source. Each spike population (low, medium, and high threshold) has a cross-sensor spike counting circuit which looks for pairs of spikes occurring within a small time window of each other. 
   Specifically, pairs of identical CF and threshold spike streams from two sensors  18  are run into a coincidence circuit  54  which in effect acts as a counter. An incoming spike for one sensor of the pair triggers the counter  54  to begin waiting for a spike from the other sensor of the pair. When a second spike occurs, a timeout circuit  56  checks to see if a sensor-geometry-specific time window has been exceeded. If the spike pair occurs within the timeout window, an ITD event is generated, and if not, no event is generated and the counter  54  is reset. This same circuit is used for every CF/spike threshold sensor pair. 
   There is one ITD feature from the ITD processor  22  for each combination of frequency channel, sensor pair, and threshold. Thus, in an embodiment having ten channels, three thresholds, and three sensor pairs, for example, there are 90 ITD features. The ITD features are combined to derive an indication of the direction of one or more sound sources, which may be a three-dimensional indication if an appropriate sensor geometry is employed. Within each channel, the signals from each threshold are combined in a weighted fashion to realize the desired wide dynamic range, as described above. The signals from different frequency bands are weighted to improve overall signal-to-noise ratio, with the weighting scheme being tailored to the application. The output(s) for each sensor pair provide an indication of the “cone of confusion” for that pair, and if more than two sensors are utilized, the outputs for the different sensor pairs can be combined to find “intersections” of the cones, removing the spatial ambiguities. 
     FIG. 12  illustrates the biomimetic IID processor  26  of  FIG. 6 . The illustrated circuitry is repeated for each frequency channel and sensor pair. The spikes from all spike populations (high, medium and low thresholds) are added together in a respective adder  58  ( 58 -A for sensor A,  58 -B for sensor B), and each sum is provided to a differencing circuit  60  to compare the sums for the sensor pair/frequency band. 
     FIG. 13  illustrates periodicity feature extraction. The block  62  illustrates the selection of input signals for a single sensor (e.g. sensor A as shown). Thus the periodicity processor  24  for a given sensor includes circuitry repeated for each frequency channel and threshold. This circuitry is shown in  FIG. 14 , and includes a coincidence circuit  64  and timeout circuit  66 . This circuitry operates in a manner similar to the ITD processor circuitry of  FIG. 11 , except that it measures intervals between temporally adjacent events for a single sensor, rather than intervals between events for a pair of sensors. 
   In many cases, downstream processing can choose the spike stream having the highest intensity for processing, improving signal-to-noise ratio. In other cases, one or more spike streams may have a higher signal-to-noise ratio than the highest-intensity stream, for example due to shadowing effects based on sensor placement and noise source location. In these cases, it makes sense to select such higher-signal-to-noise channels. 
     FIG. 15  shows a detailed implementation of a spike generator  40 . The input Vtr is responsible for establishing the threshold (high, medium or low). A comparator U 1 - 1  corresponds to the threshold crossing detector  44  of  FIG. 9 , with the remaining circuitry implementing the threshold circuit  42  and refractory circuits  46 ,  48 . 
     FIG. 16  shows a detailed implementation of an AGC circuit  34 . An amplifier U 1 A and surrounding circuitry correspond to the fast-adaptation circuitry  36  of  FIG. 8 , and an amplifier U 1 B and surrounding circuitry correspond to the slow-adaptation circuitry  38  of  FIG. 8 . 
     FIG. 17  shows a top perspective view of a housing or enclosure  68  that may be used to contain a biomimetic processing system such as that of  FIG. 1 . In the illustrated embodiment, four sensors  18  are located in respective corners of the top of the enclosure  68 , a sensor configuration corresponding to that of  FIG. 5(   a ).  FIG. 18  shows a perspective bottom view with the bottom cover removed to show a printed circuit (PC) board assembly  70  mounted within the enclosure  68 . The PC board  70  contains the various electronic components that constitute the system of  FIG. 1 , i.e., components that make up the acquisition system  10 , biomimetic processing hardware  14 , and feature processing algorithms  16 . The enclosure  68  may be particularly well suited for a mobile-mounted application such as on a mobile robot. 
   As noted above, there are a variety of potential applications for a biomimetic processing system of the general type illustrated in  FIG. 1 . Several specific applications are described in some detail below. 
   Application 1: A Mobile Study Apparatus for Frog Populations Using Breeding Calls 
   A specialized acoustic apparatus may be used to examine frog populations, which are environmentally sensitive species and good indicators of environmental health, by using a sensor system to localize and count breeding adults by their calls. The calls are known to be restricted to a small frequency range, the individual animals will all be within the confines of the pond and at its surface, the individuals are territorial and do not move often, the calls will be transient, and the range of signal level will cover roughly 40 dB. The sensor platform must be small, lightweight, and portable to reach remote areas. 
   Given the sound sources are restricted to the planar surface of the pond which is a subset of the 2D planar space, only a single pair of sensors is sufficient to resolve location of the sound sources. To maximize the ITD and ILD cues created by the sensor housing, cubes were chosen. Cubes are simple to fabricate, it is easy to orient the sensors with respect to a target area, and although impossible to characterize with closed form mathematical solutions, empirically they produce larger and more distinct ITD patterns (generally known but never formalized in the literature, extremely difficult to prove mathematically). If higher resolution and or ranging information is required two small cube arrangements can be networked and individual pair estimates can be used to triangulate sound sources. 
   Application 2: A Mobile Reconnaissance Robot 
   Autonomous vehicles and mobile robotics have become useful tools in battlefield situations removing soldiers from dangerous situations and gathering useful intelligence. Many of the tasks these field robots are required to perform either require or are greatly facilitated by acoustic detection and localization of various sound sources such as sniper fire or incoming mortar rounds. One of the more successful pieces of equipment in recent military scenarios has been the Explorer Packbot produce by Irobot fitted with a dense array of zooming optical devices. In order to focus these optical devices such as thermal zoom cameras on proper targets when conditions are at their best for a given problem, for example the moments immediately after a sniper shot to capture the thermal bloom of the muzzle blast, an acoustic localizer is essential. There are several challenges for choosing sensor placement on an Irobot platform. 
   The physical structure of the vehicle limits the maximum spacing of the sensors, there is also significant noise from motors and interfering magnetic fields from drive motors that limit the location of sensors. Because the applications are also low to the ground, issues such as ground bounce often interfere with the most useful acoustic cues. Sensor placement was chosen as high up on the vehicle as possible, on top of the vehicle sensor head, the physical head itself is used as an acoustic shield from motor noise and ground bounce. Packbot Explorer sensor heads are square by design, so sensors are placed in the corners of the head mounted nearly flush to the surface with a slight tapering collar. The flat surface of the head has ambiguity with regard to position in the upper and lower hemispheres of 3D space, but for almost all situations the vehicle with a low tactical head height only has possible sound source positions in the upper hemisphere of space. The corner mounts optimize the available space and offer several options for detection schemes. Because these robot operate in high noise, high risk environments, sensor redundancy is important not only for increasing signal to noise performance, but also to maintain function in case of sensor damage. A square arrangement of sensors allows for a more task-configurable usage of sensors: diagonal corners can be used when larger spacing is desired, T-structures can be used if a single sensor is damaged with minimal changes to processing, or combinations of all microphone pairs can be used in high noise environment to improve signal when computational resources are available. 
   Application 3: A Helmet Based System for Sniper Detection 
   As part of the Force warrior concept, individual soldiers are being fitted with the latest high tech equipment to enhance performance in battlefield conditions. A problem most soldiers face in field conditions is a loss of directional information because of helmets obscuring acoustic cues which normally would be used to localize sound sources. Acoustic sensors mounted in the surface of the helmet can be used to gather these acoustic cues and produce sound source position estimates which can be provided to the soldier through the visual display systems integrated into the next generation of helmets. 
   Helmet based applications have a constraint of size, but also have the additional complication of geometry. Many studies of geometric models from people such as Kuhn (1979) and others have shown that the geometry of a body between two acoustic sensors can greatly increase the ITD values obtained at the two sensors, and there by increase the angular resolution to which a sound source position can be determined. To a first approximation a helmet can be modeled quite well as a sphere, ideally sensors should be spaced as far apart as possible on the sphere for best performance. However, the helmet is not a sphere in that the bottom half must be open to accommodate the wearer. The best spacing with that constraint is bounded by a tetrahedral arrangement with the flat bottom surface facing the ground. This arrangement optimizes the sensor spacing condition while providing a roughly symmetric arrangement for post processing. Because of the mobility of the soldier and the variety of position a soldier make assume in the field resolution of sound sources in 3D space is required, this condition is also met by the tetrahedral arrangement. 
   Application 4: A Wall-Mounted Acoustic Tracking System 
   For additional security and surveillance in commercial applications, it is useful to have acoustic tracking technology work in conjunction with video cameras and other equipment with limited directional capabilities. Acoustic systems can be used to track sound sources and guide other equipment to the sound source to minimize ‘holes’ in sensor networks. Acoustic technologies will also function in conditions where optical equipment would fail such as high or low light conditions. Acoustic sensors can also be much less intrusive than other kinds of sensors and draw far less power, they can be used as a first line of security alerting other sensor systems to activate. 
   Application 5: A HumVee Mounted Acoustic Monitoring System 
   The HumVee is a vehicle that is often placed in high risk areas where localizing sniper fire or the direction of incoming mortar rounds would be extremely advantageous. The vehicle itself is a large noise source that can interfere with localizing a sound source. Sensor placement needs to be as far from engine and exhaust noise as possible, the best position being the roof of the vehicle. The height and position on the roof uses the vehicle as a shield for ground bounce as well as vehicle generated noise. The vehicle also is capable of traveling at relatively high speeds and makes wind noise and vehicle vibration an issue. The large area provided by the vehicle roof allows for large sensor spacings and for sensor casings large enough to accommodate vibration damping materials and windscreens. A square arrangement of sensors provides localization accuracy in the upper hemisphere of space around the vehicle which is sufficient for most vehicle conditions. The square arrangement also allows for sensor damage where any single sensor can be removed and the remaining three will provide accurate estimates. 
   Application 6: An Underwater System for Monitoring Dolphin Pod Behavior 
   Monitoring dolphins in the wild is a difficult task, but is best accomplished using acoustic methods. Undersea environments do not lend themselves to video surveillance because of the rapid fall off in light intensity, but is ideal for acoustic monitoring because of the excellent conduction of sound. More over, dolphins are extremely vocal animals that not only communicate using acoustic signals, but also navigate with them as well. 
   The arrangement of acoustic sensors for such applications have several constraints, first the structure must be open in order to minimize drift from ocean currents, second the structure must be as large as possible to compensate for increased speed of sound in water. In addition the sensors must be arranged to have good resolution in all three dimensional space as underwater environments allow for movement freely in all directions. Directional cues are used to identify animal position, vocalization identification is used track individual animals. The configuration which optimizes many of the performance to constraint value is an open strutted tetrahedral array. The tetrahedral structure is structurally strong and lends itself to being built with a minimum number of struts opening up the array housing to allow for easy water flow through the array. The structure also has equidistant terminal points producing identical sensor separation for all sensors pairs minimizing the computational complexity of the acoustic processing. 
   Beyond the above, there are several other general and specific application areas for acoustic biomimetic processing including the following: 
   Speech recognition 
   Cochlear implants 
   Population monitoring (including background monitoring as well as localizing/identifying specific members) 
   Acoustic data logging (data compression) 
   Underwater acoustic monitoring (both passive and “active” in the sense of providing a stimulus such as in sonar) 
   Additionally, the concepts disclosed herein can be applied to processing of other forms of energy such as vibrational or seismic energy, which can be utilized for tasks such as machine condition monitoring, nuclear test monitoring, and earthquake or other seismic monitoring. It may be possible to utilize the general processing scheme in conjunction with chemical sensors so as to identify and localize pollutants or other substances of interest against a background of substances. 
   While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims