Phase-Aware Single-Channel Speech Enhancement with Modulation-Domain Kalman Filtering

TL;DR

This paper introduces a phase-aware speech enhancement method using modulation-domain Kalman filtering that jointly estimates amplitude and phase, leading to improved speech quality over traditional methods across various noise conditions.

Contribution

The paper proposes a novel single-channel speech enhancement algorithm that tracks both speech amplitude and phase using Kalman filtering with circular statistics, enhancing reconstruction quality.

Findings

Outperforms traditional algorithms in various noise conditions.

Effectively tracks speech phase for better signal reconstruction.

Improves speech quality metrics across different SNRs.

Abstract

We present a single-channel phase-sensitive speech enhancement algorithm that is based on modulation-domain Kalman filtering and on tracking the speech phase using circular statistics. With Kalman filtering, using that speech and noise are additive in the complex STFT domain, the algorithm tracks the speech log-spectrum, the noise log-spectrum and the speech phase. Joint amplitude and phase estimation of speech is performed. Given the noisy speech signal, conventional algorithms use the noisy phase for signal reconstruction approximating the speech phase with the noisy phase. In the proposed Kalman filtering algorithm, the speech phase posterior is used to create an enhanced speech phase spectrum for signal reconstruction. The Kalman filter prediction models the temporal/inter-frame correlation of the speech and noise log-spectra and of the speech phase, while the Kalman filter update models their nonlinear relations. With the proposed algorithm, speech is tracked and estimated both in the log-spectral and spectral phase domains. The algorithm is evaluated in terms of speech quality and different algorithm configurations, dependent on the signal model, are compared in different noise types. Experimental results show that the proposed algorithm outperforms traditional enhancement algorithms over a range of SNRs for various noise types.