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

Abstract

Abstract-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 pursuitbased approach. The third approach is shown to perform very accurately in simulated reverberant conditions and additive noise, and its computational requirements are very small.

Real-Time Multiple Sound Source Localization and Counting Using a Circular Microphone Array

“…In our previous work [22], [31], [32], we used only the ω max i frequency, corresponding to the strongest component of the cross-power spectrum of the microphone pair {m i m i+1 } in a single-source zone, giving us a single DOA for each single-source zone. In this work we propose the use of d frequency components in each single-source zone, i.e., the use of those frequencies that correspond to the indices of the d highest peaks of the magnitude of the crosspower spectrum over all microphone pairs.…”

“…Once we have the histogram in the n th time frame (the length-L vector, y n ), our goal is to count the number of active sources and to estimate their DOAs. In our previous work, [31], [32] we performed these tasks separately, but here we combine them into a single process.…”

“…The methodology is not specific to the geometry of the array, and is based on the following steps: (a) finding single-source zones in the time-frequency domain [24] (i.e., zones where one source is clearly dominant over the others); (b) performing single-source DOA estimation on these zones using the method of [29]; (c) collecting these DOA estimations into a histogram to enable the localization of the multiple sources; and (d) jointly performing multiple DOA estimation and source counting through the post-processing of the histogram using a method based on matching pursuit [30]. Parts of this work have been recently presented in [22], [31], [32]. This current work presents a more detailed and improved methodology compared to our recently published results, especially in the following respects: (i) we provide a way of combining the tasks of source counting and DOA estimation using matching pursuit in a natural and efficient manner; and (ii) we provide a thorough performance investigation of our proposed approach in numerous simulation and real-environment scenarios, both for the DOA estimation and the source counting tasks.…”

Directional coding of audio using a circular microphone array

“…Moreover, these methods suffer from spatial aliasing above a certain spatial aliasing cutoff frequency which causes erroneous estimates and can degrade the quality of the reconstructed sound field. Our method tries to overcome these problems by employing a per time frame DOA estimation for multiple simultaneous sources (for details see [11,12,13]). Based on the estimated DOAs, spatial filtering with a fixed superdirective beamformer separates the source signals that come from different directions.…”

Abnormal acoustic event localization based on selective frequency bin in high noise environment for audio surveillance

“…These methods have shown to be effective in simultaneous localization of multiple sources. However, they require high computational load and are not applicable to wideband sources [6], [7]. Other approaches such as the Multiple SIgnal Classification (MUSIC) method [8] developed for handling wideband sources, have limitations such as the number of sources has to be strictly less than the number of sensors [9].…”