Source localization and enhancement are often treated separately in the array processing literature. One can apply steered response power (SRP) localization to determine the sources' Directions-Of-Arrival (DOA) followed by beamforming and Wiener post-filtering to isolate the sources from each other and ambient interference. We show that when there is significant overlap between directional sources of interest in the time-frequency (TF) plane, traditional SRP localization breaks down. This may occur, for example, when the array is located near a reflector, significant early reflections are present, or the sources are harmonized. We propose a joint solution to the localization and enhancement problems via a probabilistic interpretation of the SRP function. We formulate optimization procedures for (1) a mixture of single-source SRP distributions (MoSRP) and (2) a multi-source SRP distribution (MultSRP). Unlike in traditional localization, the latter approach explicitly models source overlap in the TF plane. Results shows that the MultSRP model is capable of localizing sources with significant overlap in the TF domain and that either of the proposed methods out-performs standard SRP localization for multiple speakers.

Inter-channel phase (IPD) and level (ILD) differences are common features in multichannel source separation algorithms like DUET and MENUET. However, their utility depends strongly on the configuration of the array and what microphone pairs are used to calculate them. IPDs are most useful when extracted from microphones that are close together as this avoids spatial aliasing. In contrast, ILD clusters are only well separated for widely spaced microphones. We investigate this trade-off between IPD and ILD features and propose a method to best combine them for multichannel source separation. Experimental results demonstrate the utility of this approach.

We describe multichannel blind source separation and tracking algorithms based on clustering wrapped interchannel phase difference (IPD) features. We pose the clustering problem as one of multimodal circular-linear regression and present its probabilistic formulation. Phase wrapping due to spatial aliasing is explicitly incorporated by modeling the IPD features as circular variables. We present two methods based on Expectation-Maximization (EM) and a sequential variant of RANdom SAmple Consensus (RANSAC). We show that their strengths can be combined by using RANSAC to initialize EM. The IPD clustering algorithm is applied to separate stationary speakers from a multichannel mixture. We then extend it to the case of moving speakers by tracking their directions-of-arrival with the Factorial Wrapped Kalman Filter (FWKF) using RANSAC as a data preprocessor. Experimental results demonstrate that the proposed methods perform well in the presence of reverberant babble noise and spatial aliasing. The FWKF successfully tracks and separates moving speakers with separation quality comparable to that for stationary speakers.

We address the problem of blind separation of speech signals with a microphone array. We demonstrate that a signal propagating towards the array at an angle corresponds to interchannel phase difference (IPD) data that lies on a wrapped line (i.e helix) in a circular-linear domain. Thus, the problem reduces to that of fitting helices to data that lies on a cylinder. However, outliers abound because of reverberation, noise, and signal overlap in the time-frequency domain, so we perform the clustering with a sequential variant of Random Sample Consensus (RANSAC). We show that this method can easily be applied to arrays with many microphones and that it is robust in reverberant experimental conditions.

We present the wrapped Kalman filter (WKF) for tracking the azimuth of a speaker with a compact, 3-channel microphone array. Traditional extended and unscented filters assume that the observation is a rotating vector in${\BBR^2}$. However, the azimuth inhabits a 1-D subspace: the unit circle. We model the state variable with a wrapped Gaussian distribution and show that this achieves a lower mean squared error than 2-D methods. We demonstrate the superior tracking performance of the WKF in simulated and real reverberant environments.