In this work, a multiple sound source localization and counting method is presented, that imposes relaxed sparsity constraints on the source signals. A uniform circular microphone array is used to overcome the ambiguities of linear arrays, however the underlying concepts (sparse component analysis and matching pursuit-based operation on the histogram of estimates) are applicable to any microphone array topology. Our method is based on detecting time-frequency (TF) zones where one source is dominant over the others. Using appropriately selected TF components in these “single-source” zones, the proposed method jointly estimates the number of active sources and their corresponding directions of arrival (DOAs) by applying a matching pursuit-based approach to the histogram of DOA estimates. The method is shown to have excellent performance for DOA estimation and source counting, and to be highly suitable for real-time applications due to its low complexity. Through simulations (in various signal-to-noise ratio conditions and reverberant environments) and real environment experiments, we indicate that our method outperforms other state-of-the-art DOA and source counting methods in terms of accuracy, while being significantly more efficient in terms of computational complexity.

This work proposes a novel method for 3D direction of arrival (DOA) estimation based on the sound intensity vector estimation, via the encoding of the signals of a spherical microphone array from the space domain to the spherical harmonic domain. The sound intensity vector is estimated on detected single source zones (SSZs), where one source is dominant. A smoothed 2D histogram of these estimates reveals the DOA of the present sources and through an iterative process, accurate 3D DOA information can be obtained. The performance of the proposed method is demonstrated through simulations in various signal-to-noise ratio and reverberation conditions.

We propose a novel real-time adaptative localization approach for multiple sources using a circular array, in order to suppress the localization ambiguities faced with linear arrays, and assuming a weak sound source sparsity which is derived from blind source separation methods. Our proposed method performs very well both in simulations and in real conditions at 50% real-time.

This article details and evaluates three alternative approaches to sound-field visualization, which all employ the use of spatially-localized active-intensity (SLAI) vectors. SLAI vectors are particularly interesting as they allow direction-of-arrival (DoA) estimates to be extracted in multiple spatially-localized sectors, such that sound sources and/or noise present in one sector has reduced influence on the DoA estimate made in the other sectors. These DoA estimates may then be used to...

Steered response power (SRP) techniques have been well appreciated for their robustness and accuracy in estimating the direction of arrival (DOA) when a single source is active. However, by increasing the number of sources, the complexity of the resulting power map increases, making it challenging to localize the separate sources. In this work, we propose an efficient 2D histogram processing approach which is applied on the local DOA estimates, provided by SRP, and reveals the DOA of multiple audio sources in an iterative fashion. Driven by the results, we also apply the same methodology to local DOA estimates of a known subspace method and improve its accuracy. The performance of the presented algorithms is validated with numerical simulations and real measurements with a rigid spherical microphone array in different acoustical conditions: for multiple audio sources with different angular separations, various reverberation and signal-to-noise ratio (SNR) values.