We study the cocktail-party effect, which refers to the ability of a listener to attend to a single talker in the presence of adverse acoustical conditions. It has been observed that this ability improves in the presence of binaural cues. In this paper, we explore a technique for speech segregation based on sound localization cues. The auditory masking phenomenon motivates an "ideal" binary mask in which time-frequency regions that correspond to the weak signal are canceled. In our model we estimate this binary mask by observing that systematic changes of the interaural time differences and intensity differences occur as the energy ratio of the original signals is modified. The performance of our model is comparable with results obtained using the ideal binary mask and it shows a large improvement over existing pitch-based algorithms.

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At a cocktail party, one can selectively attend to a single voice and filter out all the other acoustical interferences. How to simulate this perceptual ability remains a great challenge. This paper describes a novel, supervised learning approach to speech segregation, in which a target speech signal is separated from interfering sounds using spatial localization cues: interaural time differences (ITD) and interaural intensity differences (IID). Motivated by the auditory masking effect, the notion of an "ideal" time-frequency binary mask is suggested, which selects the target if it is stronger than the interference in a local time-frequency (T-F) unit. It is observed that within a narrow frequency band, modifications to the relative strength of the target source with respect to the interference trigger systematic changes for estimated ITD and IID. For a given spatial configuration, this interaction produces characteristic clustering in the binaural feature space. Consequently, pattern classification is performed in order to estimate ideal binary masks. A systematic evaluation in terms of signal-to-noise ratio as well as automatic speech recognition performance shows that the resulting system produces masks very close to ideal binary ones. A quantitative comparison shows that the model yields significant improvement in performance over an existing approach. Furthermore, under certain conditions the model produces large speech intelligibility improvements with normal listeners.

In a natural environment, speech signals are degraded by both reverberation and concurrent noise sources. While human listening is robust under these conditions using only two ears, current two-microphone algorithms perform poorly. The psychological process of figure-ground segregation suggests that the target signal is perceived as a foreground while the remaining stimuli are perceived as a background. Accordingly, the goal is to estimate an ideal time-frequency (T-F) binary mask, which selects the target if it is stronger than the interference in a local T-F unit. In this paper, a binaural segregation system that extracts the reverberant target signal from multisource reverberant mixtures by utilizing only the location information of target source is proposed. The proposed system combines target cancellation through adaptive filtering and a binary decision rule to estimate the ideal T-F binary mask. The main observation in this work is that the target attenuation in a T-F unit resulting from adaptive filtering is correlated with the relative strength of target to mixture. A comprehensive evaluation shows that the proposed system results in large SNR gains. In addition, comparisons using SNR as well as automatic speech recognition measures show that this system outperforms standard two-microphone beamforming approaches and a recent binaural processor.

This paper presents a novel method for tracking the azimuth locations of multiple active sources based on binaural processing. Binaural cues are strongly correlated with source locations for spectral regions dominated by only one source, Therefore, this approach integrates reliable information across different frequency channels to produce a likelihood function in the target space. Finally, a hidden Markov model (HMM) is employed for forming continuous tracks and detecting the number of active sources across time. Experimental results are presented for simulated multi-source scenarios.