Title: Phase-Aware Single-Channel Speech Enhancement with Modulation-Domain Kalman Filtering,
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.