Aviad Eisenberg

Aviad Eisenberg, Sharon Gannot, Shlomo E. Chazan

In this paper we present a unified time-frequency method for speaker extraction in clean and noisy conditions. Given a mixed signal, along with a reference signal, the common approaches for extracting the desired speaker are either applied in the time-domain or in the frequency-domain. In our approach, we propose a Siamese-Unet architecture that uses both representations. The Siamese encoders are applied in the frequency-domain to infer the embedding of the noisy and reference spectra, respectively. The concatenated representations are then fed into the decoder to estimate the real and imaginary components of the desired speaker, which are then inverse-transformed to the time-domain. The model is trained with the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) loss to exploit the time-domain information. The time-domain loss is also regularized with frequency-domain loss to preserve the speech patterns. Experimental results demonstrate that the unified approach is not only very easy to train, but also provides superior results as compared with state-of-the-art (SOTA) Blind Source Separation (BSS) methods, as well as commonly used speaker extraction approach.

Aviad Eisenberg, Sharon Gannot, Shlomo E. Chazan

This paper introduces a multi-microphone method for extracting a desired speaker from a mixture involving multiple speakers and directional noise in a reverberant environment. In this work, we propose leveraging the instantaneous relative transfer function (RTF), estimated from a reference utterance recorded in the same position as the desired source. The effectiveness of the RTF-based spatial cue is compared with direction of arrival (DOA)-based spatial cue and the conventional spectral embedding. Experimental results in challenging acoustic scenarios demonstrate that using spatial cues yields better performance than the spectral-based cue and that the instantaneous RTF outperforms the DOA-based spatial cue.