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    • Division of Electrical, Electronics, and Computer Science (EECS)
    • Electrical Engineering (EE)
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    Binaural Source Localization using subband reliability and interaural time difference patterns

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    Thesis full text (4.364Mb)
    Author
    Karthik, Girija Ramesan
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    Abstract
    Machine localization of sound sources is necessary for a wide range of appli- cations, including human-robot interaction, surveillance and hearing aids. Robot sound localization algorithms have been proposed using microphone arrays with varied number of microphones. Adding more microphones helps increase the localization performance as more spatial cues can be obtained based on the number and arrangement of the microphones. However, hu- mans have an incredible ability to accurately localize and attend to target sound sources even in adverse noise conditions. The perceptual organi- zation of sounds in complex auditory scenes is done using various cues that help us group/segregate sounds. Among these, two major spatial cues are the Interaural time difference (ITD) and Interaural level/intensity difference(ILD/IID). Popular algorithms, for binaural source localization, model the distributions of ITD and ILD in each frequency subband us- ing Gaussian Mixture Models (GMMs) and perform likelihood integration across the time-frequency plane to estimate the direction of arrival (DoA) of the sources. In this thesis, we use ITDs and show that the localization performance of a GMM based scheme varies across subbands. We pro- pose subband selection and subband weighting schemes in order to exploit the subband reliability for localization. Source localization results demon- strate that the proposed schemes perform better than uniformly weighing all subbands. In particular, the best set of weights closely correspond to the case of selecting only the most reliable subband. We also propose a new binaural localization technique in which templates, that capture the direction-speci c interaural time di erence patterns, are used to localize sources. These templates are obtained using histograms of ITDs in each subband. DoA is estimated using a template matching scheme, which is experimentally found to perform better than the GMM based scheme.
    URI
    https://etd.iisc.ac.in/handle/2005/5189
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    • Electrical Engineering (EE) [357]

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