USE OF MICROPHONE DIRECTIVITY FOR THE LOCALLIZATION OF SOUND SOURCES

In a recent paper [1] the proof-of-concept of a novel approach for the localization of sound source was demonstrated. The method relies on the use of unidirectional microphones and amplitude-based signals' features to extract information about the direction of the incoming sound. By intersecting the directions identified by a pair of unidirectional microphones, the position of the emitting source can be identified.In this study we expand the work presented in that paper by assessing the effectiveness of the approach for the localization of an acoustic source in an indoor setting. As the method relies on the accurate knowledge of the microphones directivity, analytical expression of the acoustic sensors polar pattern were derived by testing them in an anechoic chamber. Then an experiment was conducted in a classroom-type environment by using an array of three unidirectional microphones. The ability to locate the position of a commercial speaker placed at different position is discussed.It is believed that this method may pave the road toward a new generation of reduced size sound detectors and localizers

Estimating uncertainty models for speech source localization in real-world environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 131-140).This thesis develops improved solutions to the problems of audio source localization and speech source separation in real reverberant environments. For source localization, it develops a new time- and frequency-dependent weighting function for the generalized cross-correlation framework for time delay estimation. This weighting function is derived from the speech spectrogram as the result of a transformation designed to optimally predict localization cue accuracy. By structuring the problem in this way, we take advantage of the nonstationarity of speech in a way that is similar to the psychoacoustics of the precedence effect. For source separation, we use the same weighting function as part of a simple probabilistic generative model of localization cues. We combine this localization cue model with a mixture model of speech log-spectra and use this combined model to do speech source separation. For both source localization and source separation, we show significantly performance improvements over existing techniques on both real and simulated data in a range of acoustic environments.by Kevin William Wilson.Ph.D