Stereo Speech Enhancement Using Custom Mid-Side Signals and Monaural Processing

TL;DR

This paper introduces Custom Mid-Side Signals (CMSS), a novel stereo representation enabling existing monaural speech enhancement systems to effectively process stereo signals, improving speech quality with reduced computational cost.

Contribution

The paper proposes CMSS, a new stereo signal representation that extends mid-side benefits to diverse stereo inputs, allowing monaural SE systems to operate efficiently on stereo content.

Findings

CMSS improves speech quality in stereo enhancement.

Processing with CMSS reduces computational cost by about half.

Subjective tests show preference for CMSS-processed speech.

Abstract

Speech Enhancement (SE) systems typically operate on monaural input and are used for applications including voice communications and capture cleanup for user generated content. Recent advancements and changes in the devices used for these applications are likely to lead to an increase in the amount of two-channel content for the same applications. However, SE systems are typically designed for monaural input; stereo results produced using trivial methods such as channel independent or mid-side processing may be unsatisfactory, including substantial speech distortions. To address this, we propose a system which creates a novel representation of stereo signals called Custom Mid-Side Signals (CMSS). CMSS allow benefits of mid-side signals for center-panned speech to be extended to a much larger class of input signals. This in turn allows any existing monaural SE system to operate as an efficient stereo system by processing the custom mid signal. We describe how the parameters needed for CMSS can be efficiently estimated by a component of the spatio-level filtering source separation system. Subjective listening using state-of-the-art deep learning-based SE systems on stereo content with various speech mixing styles shows that CMSS processing leads to improved speech quality at approximately half the cost of channel-independent processing.