Source counting in real-time sound source localization using a circular microphone array

Recently, we proposed an approach inspired by Sparse Component Analysis for real-time localization of multiple sound sources using a circular microphone array. The method was based on identifying time-frequency zones where only one source is active, reducing the problem to single-source localization for these zones. A histogram of estimated Directions of Arrival (DOAs) was formed and then processed to obtain improved DOA estimates, assuming that the number of sources was known. In this paper, we extend our previous work by proposing three different methods for counting the number of sources by looking for prominent peaks in the derived histogram based on: (a) performing a peak search, (b) processing an LPC-smoothed version of the histogram, (c) employing a matching pursuit-based approach. The third approach is shown to perform very accurately in simulated reverberant conditions and additive noise, and its computational requirements are very small.

[1]  Emmanuel Vincent,et al.  Multi-source TDOA estimation in reverberant audio using angular spectra and clustering , 2012, Signal Process..

[2]  Athanasios Mouchtaris,et al.  Real-time multiple sound source localization using a circular microphone array based on single-source confidence measures , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Hiroshi Ishiguro,et al.  Evaluation of a MUSIC-based real-time sound localization of multiple sound sources in real noisy environments , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Eric A. Lehmann,et al.  Diffuse Reverberation Model for Efficient Image-Source Simulation of Room Impulse Responses , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[5]  M. Viberg,et al.  Two decades of array signal processing research: the parametric approach , 1996, IEEE Signal Process. Mag..

[6]  Hagit Messer,et al.  Submitted to Ieee Transactions on Signal Processing Detection of Signals by Information Theoretic Criteria: General Asymptotic Performance Analysis , 2022 .

[7]  Yannick Deville,et al.  A new time-frequency correlation-based source separation method for attenuated and time-shifted mixtures , 2007 .

[8]  Scott Rickard,et al.  Blind separation of speech mixtures via time-frequency masking , 2004, IEEE Transactions on Signal Processing.

[9]  Greg Hamerly,et al.  Learning the k in k-means , 2003, NIPS.

[10]  Jacob Benesty,et al.  Time Delay Estimation in Room Acoustic Environments: An Overview , 2006, EURASIP J. Adv. Signal Process..

[11]  Pierre Comon,et al.  Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .

[12]  Nedelko Grbic,et al.  Source localization for multiple speech sources using low complexity non-parametric source separation and clustering , 2011, Signal Process..

[13]  Akihiko Sugiyama,et al.  A new DOA estimation method using a circular microphone array , 2007, 2007 15th European Signal Processing Conference.