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The performance of most of the classical sound source localization algorithms degrades seriously in the presence of background noise or reverberation. Recently, deep neural networks DNNs have successfully been applied to sound source localization, which mainly aim to classify the direction-of-arrival DoA into one of the candidate sectors. In this paper, we propose a DNN-based phase difference enhancement for DoA estimation, which turned out to be better than the direct estimation of the DoAs from the input interchannel phase differences IPDs. The sinusoidal functions of the phase differences for “clean and dry” source signals are estimated from the sinusoidal functions of the IPDs for the input signals, which may include directional signals, diffuse noise, and reverberation. The resulted DoA is further refined to compensate for the estimation bias near the end-fire directions. From the enhanced IPDs, we can determine the DoA for each frequency bin and the DoAs for the current frame from the distributions of the DoAs for frequencies. Experimental results with various types and levels of background noise, reverberation times, numbers of sources, room impulse responses, and DoAs showed that the proposed method outperformed conventional approaches.

Multichannel speech reinforcement exploiting DoA information is proposed.An empirical evidence of the binaural unmasking for monaural speech is provided.Proposed reinforcement restores perceived loudness considering binaural unmasking.The performance of the algorithm is verified through subjective listening tests. Speech reinforcement or near-end listening enhancement is a technique that modifies the far-end signal to mitigate the effect of the near-end noise, usually based on the power spectra of the far-end signal and the near-end noise. Psychoacoustic experiments have shown that the location of a noise source with respect to that of a signal source affects the amount of masking. Since conventional speech reinforcement methods obtain spectral gain based only on the power spectra, this psychoacoustic phenomenon called binaural unmasking has not been considered in those approaches. In this paper, we propose a novel speech reinforcement algorithm that modifies the far-end speech signal based on both the power spectrum and the direction-of-arrival (DoA) of the noise. Specifically, we have computed the equivalent frontal noise level from the observed noise level and the estimated DoA, and used it to compute spectral gains as in conventional partial loudness restoration-based speech reinforcement. Experimental results showed that the proposed method outperformed the conventional methods based on partial loudness restoration and speech intelligibility index (SII) optimization in terms of the overall perceived quality through subjective listening tests.