Soumitro Chakrabarty

Extended Kalman filter with probabilistic data association for multiple non-concurrent speaker localization in reverberant environments

Acoustic source localization and tracking (ASLT) in reverberant environments is a challenging task due to the multi-path propagation of acoustic waves. ASLT is often based on the use of a Kalman filter or a particle filter, with time-difference-of-arrival (TDOA) estimates used as measurements. In this work, we aim to track non-concurrent speakers by applying an extended Kalman filter (EKF) with probabilistic data association (PDA) that takes into account multiple measurements simultaneously. By using PDA, the inaccuracy of the measurements caused by room reflections and noise is explicitly taken into account. Unlike in typical approaches where the measurements consist of broadband TDOA estimates, the measurements in the proposed approach consist of multiple narrowband direction-of-arrival (DOA) estimates obtained from distributed microphone arrays. Experimental results demonstrate that incorporating PDA and using properly selected narrowband DOA estimates leads to a better tracking performance, as compared to the standard EKF with a single narrowband or broadband measurement.

On the Numerical Instability of an LCMV Beamformer for a Uniform Linear Array

We analyze the conditions for numerical instability in the solution of a linearly constrained minimum variance (LCMV) beamformer with multiple directional constraints for a uniform linear array. An analytic expression is presented to determine the frequencies (for broadband signals such as speech) where the inverse term in the solution of the LCMV beamformer does not exist. Simulation results and power patterns are provided to further illustrate the problem. In addition, we investigate and discuss possible solutions to the problem.

A Bayesian approach to spatial filtering and diffuse power estimation for joint dereverberation and noise reduction

A spatial filter, with L linear constraints that are based on instantaneous narrowband direction-of-arrival (DOA) estimates, was recently proposed to obtain a desired spatial response for at most L sound sources. In noisy and reverberant environments, it becomes difficult to get reliable instantaneous DOA estimates and hence obtain the desired spatial response. In this work, we develop a Bayesian approach to spatial filtering that is more robust to DOA estimation errors. The resulting filter is a weighted sum of spatial filters pointed at a discrete set of DOAs, with the relative contribution of each filter determined by the posterior distribution of the discrete DOAs given the microphone signals. In addition, the proposed spatial filter is able to reduce both reverberation and noise. In this work, the required diffuse sound power is estimated using the posterior distribution of the discrete set of DOAs. Simulation results demonstrate the ability of the proposed filter to achieve strong suppression of the undesired signal components with small amount of signal distortion, in noisy and reverberant conditions.

A Bayesian Approach to Informed Spatial Filtering With Robustness Against DOA Estimation Errors

A Bayesian approach to spatial filtering is presented, which is robust to uncertain or erroneous direction-of-arrival (DOA) information. The proposed framework aims to capture multiple sound sources at each time-frequency instant with an arbitrary direction-dependent gain, while attenuating diffuse sound and noise. For robustness, the DOA corresponding to each sound source is assumed to be a discrete random variable with a prior defined on a discrete set of candidate DOAs over the whole DOA space. With this assumption, the desired spatial filter is given as a weighted sum of spatial filters corresponding to a specific combination of probable DOA values, where the weights are given by the joint posterior probabilities of the combination of DOA values. Assuming the whole DOA space as the support for each random variable results in redundant computations and contributes to a high computational cost. To alleviate this problem, a narrowband DOA estimate-based posterior probability approximation method is proposed, which isolates regions in the DOA space with high probability of containing the actual source DOAs to compute time-adaptive supports for each random variable. Through experimental analysis, we demonstrate the robustness of the proposed framework against DOA estimation errors. Experimental evaluation with simulated and measured room impulse responses, in terms of objective performance measures, demonstrates the effectiveness of the framework to perform spatial filtering in noisy and reverberant acoustic environments.

Head-orientation compensation with video-informed single channel speech enhancement

It has been shown that human speakers do not radiate voice sound uniformly in all directions and that the radiation pattern is frequency dependent. As a consequence, the quality of the speech signal acquired by distant microphones depends on the relative orientation of the head with respect to the microphone. In this paper, a single channel speech enhancement framework is proposed that incorporates the head orientation information to compensate for the reduction in sound energy due to the relative orientation of the speaker with respect to the microphone, while attenuating the noise. In the proposed framework, the head orientation at each time instance, which can potentially be estimated using computer vision techniques, is used to compute the frequency dependent gain factor that needs to be applied to compensate for the head orientation. The computed gain is then incorporated in a single channel filter which simultaneously suppresses the noise. Based on experimental evaluations, with both simulated and measured data, we demonstrate the ability of the proposed system to improve the quality of the acquired speech signal.