Modified Parametric Multichannel Wiener Filter \\for Low-latency Enhancement of Speech Mixtures with Unknown Number of Speakers

TL;DR

This paper presents Mod-PMWF, a low-latency online beamforming algorithm that adaptively enhances speech mixtures with unknown and varying numbers of speakers without needing explicit attribute estimation.

Contribution

The paper introduces a novel Mod-PMWF algorithm that adaptively forms directivity patterns for speech enhancement without estimating mixture attributes.

Findings

Effective interference reduction demonstrated

Improved subjective listening test results

Suitable for real-time speech enhancement

Abstract

This paper introduces a novel low-latency online beamforming (BF) algorithm, named Modified Parametric Multichannel Wiener Filter (Mod-PMWF), for enhancing speech mixtures with unknown and varying number of speakers. Although conventional BFs such as linearly constrained minimum variance BF (LCMV BF) can enhance a speech mixture, they typically require such attributes of the speech mixture as the number of speakers and the acoustic transfer functions (ATFs) from the speakers to the microphones. When the mixture attributes are unavailable, estimating them by low-latency processing is challenging, hindering the application of the BFs to the problem. In this paper, we overcome this problem by modifying a conventional Parametric Multichannel Wiener Filter (PMWF). The proposed Mod-PMWF can adaptively form a directivity pattern that enhances all the speakers in the mixture without explicitly estimating these attributes. Our experiments will show the proposed BF's effectiveness in interference reduction ratios and subjective listening tests.