CONSTANT FALSE ALARM RATE PERFORMANCE OF SOUND SOURCE DETECTION WITH TIME DELAY OF ARRIVAL ALGORITHM

Time Delay of Arrival (TDOA) based algorithms and Steered Response Power (SRP) based algorithms are two most commonly used methods for sound source detection and localization. SRP is more robust under high reverberation and multi-target conditions, while TDOA is less computationally intensive. This thesis introduces a modified TDOA algorithm, TDOA delay table search (TDOA-DTS), that has more stable performance than the original TDOA, and requires only 4% of the SRP computation load for a 3-dimensional space of a typical room. A 2-step adaptive thresholding procedure based on a Weibull noise peak distributions for the cross-correlations and a binomial distribution for combing potential peaks over all microphone pairs for the final detection. The first threshold limits the potential target peaks in the microphone pair cross-correlations with a user-defined false-alarm (FA) rates. The initial false-positive peak rate can be set to a higher level than desired for the final FA target rate so that high accuracy is not required of the probability distribution model (where model errors do not impact FA rates as they work for threshold set deep into the tail of the curve). The final FA rate can be lowered to the actual desired value using an M out of N (MON) rule on significant correlation peaks from different microphone pairs associated is a point in the space of interest. The algorithm is tested with simulated and real recorded data to verify resulting FA rates are consistent with the user-defined rates down to 10-6

STATISTICAL MODELS FOR CONSTANT FALSE-ALARM RATE THRESHOLD ESTIMATION IN SOUND SOURCE DETECTION SYSTEMS

Constant False Alarm Rate (CFAR) Processors are important for applications where thousands of detection tests are made per second, such as in radar. This thesis introduces a new method for CFAR threshold estimation that is particularly applicable to sound source detection with distributed microphone systems. The novel CFAR Processor exploits the near symmetry about 0 for the acoustic pixel values created by steered-response coherent power in conjunction with a partial whitening preprocessor to estimate thresholds for positive values, which represent potential targets. To remove the low frequency components responsible for degrading CFAR performance, fixed and adaptive high-pass filters are applied. A relation is proposed and it tested the minimum high-pass cut-off frequency and the microphone geometry. Experimental results for linear, perimeter and planar arrays illustrate that for desired false alarm (FA) probabilities ranging from 10-1 and 10-6, a good CFAR performance can be achieved by modeling the coherent power with Chi-square and Weibull distributions and the ratio of desired over experimental FA probabilities can be limited within an order of magnitude

Acoustic Modeling Of A Uas For Use In A Hostile Fire Detection System

Unmanned Aerial System (UAS) usage has continually increased in recent years for both recreational and military applications. One particular military application being researched is utilizing a UAS as a host platform for Hostile Fire Detection Systems (HFDS), with particular interest being focused on multi-rotor drone platforms. The type of HFDS considered in this work is based upon acoustic sensors. An acoustic based HFDS utilizes an array of microphones to measure acoustic data and then applies signal processing algorithms to determine if a transient signal is present and if present then estimates the direction from which the sound arrived. The main issue with employing an acoustic based HFDS on a multi-rotor drone is the high level of background noise due to motors, propellers, and flow noise. In this thesis a study of the acoustic near field, particularly relevant to microphones located on the drone, was performed to understand the noise produced by the UAS. More specifically, the causes and characteristics of the sources of noise were identified. The noise characteristics were then used to model the noise sources for multiple motor assemblies based upon position of the microphone and revolutions per minute (RPM) of the motors. Lastly, signal processing techniques were implemented to identify if transient signals are present and if present estimate the direction from which the sound arrives

VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network

We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-base vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the "bumpiness" of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles.Fil: Goldberg, David H.. Johns Hopkins University; Estados UnidosFil: Andreou, Andreas. Johns Hopkins University; Estados UnidosFil: Julian, Pedro Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Pouliquen, Philippe O.. Johns Hopkins University; Estados UnidosFil: Riddle, Laurence. Signal Systems Corporation; Estados UnidosFil: Rosasco, Rich. Signal Systems Corporation; Estados Unido

AUDIO SCENE SEGEMENTATION USING A MICROPHONE ARRAY AND AUDITORY FEATURES

Auditory stream denotes the abstract effect a source creates in the mind of the listener. An auditory scene consists of many streams, which the listener uses to analyze and understand the environment. Computer analyses that attempt to mimic human analysis of a scene must first perform Audio Scene Segmentation (ASS). ASS find applications in surveillance, automatic speech recognition and human computer interfaces. Microphone arrays can be employed for extracting streams corresponding to spatially separated sources. However, when a source moves to a new location during a period of silence, such a system loses track of the source. This results in multiple spatially localized streams for the same source. This thesis proposes to identify local streams associated with the same source using auditory features extracted from the beamformed signal. ASS using the spatial cues is first performed. Then auditory features are extracted and segments are linked together based on similarity of the feature vector. An experiment was carried out with two simultaneous speakers. A classifier is used to classify the localized streams as belonging to one speaker or the other. The best performance was achieved when pitch appended with Gammatone Frequency Cepstral Coefficeints (GFCC) was used as the feature vector. An accuracy of 96.2% was achieved

PERFORMANCE ANALYSIS OF SRCP IMAGE BASED SOUND SOURCE DETECTION ALGORITHMS

Steered Response Power based algorithms are widely used for finding sound source location using microphone array systems. SRCP-PHAT is one such algorithm that has a robust performance under noisy and reverberant conditions. The algorithm creates a likelihood function over the field of view. This thesis employs image processing methods on SRCP-PHAT images, to exploit the difference in power levels and pixel patterns to discriminate between sound source and background pixels. Hough Transform based ellipse detection is used to identify the sound source locations by finding the centers of elliptical edge pixel regions typical of source patterns. Monte Carlo simulations of an eight microphone perimeter array with single and multiple sound sources are used to simulate the test environment and area under receiver operating characteristic (ROCA) curve is used to analyze the algorithm performance. Performance was compared to a simpler algorithm involving Canny edge detection and image averaging and an algorithms based simply on the magnitude of local maxima in the SRCP image. Analysis shows that Canny edge detection based method performed better in the presence of coherent noise sources

An artificial patient for pure-tone audiometry

Abstract The successful treatment of hearing loss depends on the individual practitioner's experience and skill. So far, there is no standard available to evaluate the practitioner's testing skills. To assess every practitioner equally, the paper proposes a first machine, dubbed artificial patient (AP), mimicking a real patient with hearing impairment operating in real time and real environment. Following this approach, we develop a multiple-input multiple-output auditory model that synthesizes various types of hearing loss as well as elements from psychoacoustics such as false response and reaction time. The model is then used to realize a hardware implementation, comprising acoustic and vibration sensors, sound cards, and a fanless personal computer. The AP returns a feedback signal to the practitioner upon perceiving a valid test tone at the hearing threshold analogous to a real patient. The AP is derived within a theoretical framework in contrast to many other solutions. The AP handles masked air-conduction and bone-conduction hearing levels in the range from 5 to 80 dB and from – 20 to 70 dB, respectively, both at 1 kHz. The frequency range is confined within 250 and 8000 Hz. The proposed approach sets a new quality standard for evaluating practitioners