Speech enhancement using modulation-domain Kalman filtering with active speech level normalized log-spectrum global priors

Abstract

Abstract-We describe a single-channel speech enhancement algorithm that is based on modulation-domain Kalman filtering that tracks the inter-frame time evolution of the speech logpower spectrum in combination with the long-term average speech log-spectrum. We use offline-trained log-power spectrum global priors incorporated in the Kalman filter prediction and update steps for enhancing noise suppression. In particular, we train and utilize Gaussian mixture model priors for speech in the log-spectral domain that are normalized with respect to the active speech level. The Kalman filter update step uses the log-power spectrum global priors together with the local priors obtained from the Kalman filter prediction step. The logspectrum Kalman filtering algorithm, which uses the theoretical phase factor distribution and improves the modeling of the modulation features, is evaluated in terms of speech quality. Different algorithm configurations, dependent on whether global priors and/or Kalman filter noise tracking are used, are compared in various noise types.

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

“…Non-linear adaptive modulation-domain Kalman filtering algorithms can be used for speech enhancement, i.e. noise suppression and dereverberation, as in [1], [2], [3], [4] and [5]. Modulation-domain Kalman filtering can be applied for both noise and late reverberation suppression; in [2], [1], [3] and [4], various model-based speech enhancement algorithms that perform modulation-domain Kalman filtering are designed, implemented and tested.…”

“…Modulation-domain Kalman filtering can be applied for both noise and late reverberation suppression; in [2], [1], [3] and [4], various model-based speech enhancement algorithms that perform modulation-domain Kalman filtering are designed, implemented and tested. The model-based speech enhancement algorithm presented in [2] tracks and estimates the clean speech phase and the STFT-based algorithm described in [5] uses the active speech level estimator presented in [6].…”

“…Noise tracking is performed in [47] and subsequently in [49]. In the KF update step, the correlation between speech and noise samples can be estimated, as in [3], [2] and [5].…”

Speech Enhancement Using Kalman Filtering in the Logarithmic Bark Power Spectral Domain

“…The phase factor in STFT bins, ↵ k , is given by ↵ k = cos( (k) (k)), as in [9], [7] and [8]. For clarity, we omit the time-frame index, t, below and we only include it in equations involving multiple frames.…”

“…We approximate the posterior of the speech and noise spectral log-powers as a two-dimensional Gaussian distribution with a full covariance matrix using the probability distribution of the phase factor in Bark bands. The phasesensitive KF update step computes the first two moments of the posterior distribution, [8], [7], [9], suppressing noise.…”