In this work, we address an acoustic beamforming application where two speakers are simultaneously active. We construct one subband domain beamformer in generalized sidelobe canceller (GSC) configuration for each source. In contrast to normal practice, we then jointly adjust the active weight vectors of both GSCs to obtain two output signals with minimum mutual information (MMI). In order to calculate the mutual information of the complex subband snapshots, we consider four probability density functions (pdfs), namely the Gaussian, Laplace, K0and Г pdfs. The latter three belong to the class of super-Gaussian density functions that are typically used in independent component analysis as opposed to conventional beamforming. We demonstrate the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on data from the PASCAL Speech Separation Challenge. In the experiments, the delay-and-sum beamformer achieved a word error rate (WER) of 70.4 %. The MMI beamformer under a Gaussian assumption achieved 55.2 % WER which was further reduced to 52.0 % with a K0pdf, whereas the WER for data recorded with close-talking microphone was 21.6 %.

In this paper, we consider an acoustic beamforming application where two speakers are simultaneously active. We construct one subband-domain beamformer in generalized sidelobe canceller (GSC) configuration for each source. In contrast to normal practice, we then jointly optimize the active weight vectors of both GSCs to obtain two output signals with minimum mutual information (MMI). Assuming that the subband snapshots are Gaussian-distributed, this MMI criterion reduces to the requirement that the cross-correlation coefficient of the subband outputs of the two GSCs vanishes. We also compare separation performance under the Gaussian assumption with that obtained from several super-Gaussian probability density functions (pdfs), namely, the Laplace$K_0$and$\Gamma$pdfs. Our proposed technique provides effective nulling of the undesired source, but without the signal cancellation problems seen in conventional beamforming. Moreover, our technique does not suffer from the source permutation and scaling ambiguities encountered in conventional blind source separation algorithms. We demonstrate the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on data from the PASCAL Speech Separation Challenge (SSC). On the SSC development data, the simple delay-and-sum beamformer achieves a word error rate (WER) of 70.4%. The MMI beamformer under a Gaussian assumption achieves a 55.2% WER, which is further reduced to 52.0% with a$K_0$pdf, whereas the WER for data recorded with a close-talking microphone is 21.6%.

In prior work, we developed a speaker tracking system based on an extended Kalman filter using time delays of arrival (TDOAs) as acoustic features. While this system functioned well, its util-ity was limited to scenarios in which a single speaker was to be tracked. In this work, we remove this restriction by generalizing the IEKF, first to a probabilistic data association filter, which in-corporates a clutter model for rejection of spurious acoustic events, and then to a joint probabilistic data association filter (JPDAF), which maintains a separate state vector for each active speaker. In a set of experiments conducted on seminar and meeting data, the JPDAF speaker tracking system reduced the multiple object track-ing errror from 20.7 % to 14.3 % with respect to the IEKF system. In a set of automatic speech recognition experiments conducted on the output of a 64 channel microphone array which was beam-formed using automatic speaker position estimates, applying the JPDAF tracking system reduced word error rate from 67.3 % to 66.0%. Moreover, the word error rate on the beamformed output was 13.0 % absolute lower than on a single channel of the array. Index Terms: acoustic source localization, Kalman filter, person tracking, far-field speech recognition, microphone arrays

In this paper, we present a novel approach for tracking a lecturer during the course of his speech. We use features from multiple cameras and microphones, and process them in a joint particle filter framework. The filter performs sampled projections of 3D location hypotheses and scores them using features from both audio and video. On the video side, the features are based on foreground segmentation, multi-view face detection and upper body detection. On the audio side, the time delays of arrival between pairs of microphones are estimated with a generalized cross correlation function. Computationally expensive features are evaluated only at the particles' projected positions in the respective camera images, thus the complexity of the proposed algorithm is low. We evaluated the system on data that was recorded during actual lectures. The results of our experiments were 36 cm average error for video only tracking, 46 cm for audio only, and 31 cm for the combined audio-video system.

In this work, we propose an algorithm for acoustic source localization based on time delay of arrival (TDOA) estimation.In earlier work by other authors, an initial closed-form approximation was first used to estimate the true position of the speaker followed by a Kalman filtering stage to smooth the time series of estimates.In the proposed algorithm, this closed-form approximation is eliminated by employing a Kalman filter to directly update the speaker position estimate based on the observed TDOAs.In particular, the TDOAs comprise the observation associated with an extended Kalman filter whose state corresponds to the speaker position.We tested our algorithm on a data set consisting of seminars held by actual speakers.Our experiments revealed that the proposed algorithm provides source localization accuracy superior to the standard spherical and linear intersection techniques.Moreover, the proposed algorithm, although relying on an iterative optimization scheme, proved efficient enough for real-time operation.