Position estimation of binaural sound source in reverberant environments

Most binaural sound source systems perform localization in either direction or distance perception. However, in real scenarios both perceptions are important to estimate source position in various environment conditions especially with the rapid technological growth in smart machines and their involvement in human daily life. This paper introduces an approach for azimuth and distance of binaural sound source localization in different reverberating environments using only two microphones. The algorithm is based on statistical features of the binaural cues and the difference of the binaural magnitude spectra of the binaural signal. Gaussian Mixture Models (GMMs) are used to jointly learn both distances and azimuths in different reverberant rooms. The proposed system does not require any prior knowledge of head related transfer function (HRTF), acoustical environment or room parameters. The performance has been evaluated at different aspects and conditions and reported effective and robust results, especially in the case of training set mismatch.