Sub-sampling-based 2D localization of an impulsive acoustic source in reverberant environments

ABSTRACT: This paper presents a robust method for two-dimensional (2D) impulsive acoustic source localization in a room environment using low sampling rates. The proposed method finds the time delay from the room impulse response (RIR) which makes it robust against room reverberations. We consider the RIR as a sparse phenomenon and apply a recently proposed sparse signal reconstruction technique called orthogonal clustering (OC) for its estimation from the sub-sampled received signal. The arrival time of the direct path signal at a pair of microphones is identified from the estimated RIR, and their difference yields the desired time delay estimate (TDE). Low sampling rates reduces the hardware and computational complexity and decreases the communication between the microphones and the centralized location. Simulation and experimental results of an actual hardware setup are presented to demonstrate the performance of the proposed technique

Inference of Room Geometry From Acoustic Impulse Responses

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Sound Event Localization, Detection, and Tracking by Deep Neural Networks

In this thesis, we present novel sound representations and classification methods for the task of sound event localization, detection, and tracking (SELDT). The human auditory system has evolved to localize multiple sound events, recognize and further track their motion individually in an acoustic environment. This ability of humans makes them context-aware and enables them to interact with their surroundings naturally. Developing similar methods for machines will provide an automatic description of social and human activities around them and enable machines to be context-aware similar to humans. Such methods can be employed to assist the hearing impaired to visualize sounds, for robot navigation, and to monitor biodiversity, the home, and cities. A real-life acoustic scene is complex in nature, with multiple sound events that are temporally and spatially overlapping, including stationary and moving events with varying angular velocities. Additionally, each individual sound event class, for example, a car horn can have a lot of variabilities, i.e., different cars have different horns, and within the same model of the car, the duration and the temporal structure of the horn sound is driver dependent. Performing SELDT in such overlapping and dynamic sound scenes while being robust is challenging for machines. Hence we propose to investigate the SELDT task in this thesis and use a data-driven approach using deep neural networks (DNNs). The sound event detection (SED) task requires the detection of onset and offset time for individual sound events and their corresponding labels. In this regard, we propose to use spatial and perceptual features extracted from multichannel audio for SED using two different DNNs, recurrent neural networks (RNNs) and convolutional recurrent neural networks (CRNNs). We show that using multichannel audio features improves the SED performance for overlapping sound events in comparison to traditional single-channel audio features. The proposed novel features and methods produced state-of-the-art performance for the real-life SED task and won the IEEE AASP DCASE challenge consecutively in 2016 and 2017. Sound event localization is the task of spatially locating the position of individual sound events. Traditionally, this has been approached using parametric methods. In this thesis, we propose a CRNN for detecting the azimuth and elevation angles of multiple temporally overlapping sound events. This is the first DNN-based method performing localization in complete azimuth and elevation space. In comparison to parametric methods which require the information of the number of active sources, the proposed method learns this information directly from the input data and estimates their respective spatial locations. Further, the proposed CRNN is shown to be more robust than parametric methods in reverberant scenarios. Finally, the detection and localization tasks are performed jointly using a CRNN. This method additionally tracks the spatial location with time, thus producing the SELDT results. This is the first DNN-based SELDT method and is shown to perform equally with stand-alone baselines for SED, localization, and tracking. The proposed SELDT method is evaluated on nine datasets that represent anechoic and reverberant sound scenes, stationary and moving sources with varying velocities, a different number of overlapping sound events and different microphone array formats. The results show that the SELDT method can track multiple overlapping sound events that are both spatially stationary and moving

A computational model of spatial hearing

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 60-62).by Keith Dana Martin.M.S

Electrophysiologic assessment of (central) auditory processing disorder in children with non-syndromic cleft lip and/or palate

Session 5aPP - Psychological and Physiological Acoustics: Auditory Function, Mechanisms, and Models (Poster Session)Cleft of the lip and/or palate is a common congenital craniofacial malformation worldwide, particularly non-syndromic cleft lip and/or palate (NSCL/P). Though middle ear deficits in this population have been universally noted in numerous studies, other auditory problems including inner ear deficits or cortical dysfunction are rarely reported. A higher prevalence of educational problems has been noted in children with NSCL/P compared to craniofacially normal children. These high level cognitive difficulties cannot be entirely attributed to peripheral hearing loss. Recently it has been suggested that children with NSCLP may be more prone to abnormalities in the auditory cortex. The aim of the present study was to investigate whether school age children with (NSCL/P) have a higher prevalence of indications of (central) auditory processing disorder [(C)APD] compared to normal age matched controls when assessed using auditory event-related potential (ERP) techniques. School children (6 to 15 years) with NSCL/P and normal controls with matched age and gender were recruited. Auditory ERP recordings included auditory brainstem response and late event-related potentials, including the P1-N1-P2 complex and P300 waveforms. Initial findings from the present study are presented and their implications for further research in this area —and clinical intervention—are outlined. © 2012 Acoustical Society of Americapublished_or_final_versio