2 research outputs found
On Single-Channel Speech Enhancement and On Non-Linear Modulation-Domain Kalman Filtering
This report focuses on algorithms that perform single-channel speech
enhancement. The author of this report uses modulation-domain Kalman filtering
algorithms for speech enhancement, i.e. noise suppression and dereverberation,
in [1], [2], [3], [4] and [5]. Modulation-domain Kalman filtering can be
applied for both noise and late reverberation suppression and 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 enhancement algorithm in [2] estimates and tracks the speech phase.
The short-time-Fourier-transform-based enhancement algorithm in [5] uses the
active speech level estimator presented in [6]. This report describes how
different algorithms perform speech enhancement and the algorithms discussed in
this report are addressed to researchers interested in monaural speech
enhancement. The algorithms are composed of different processing blocks and
techniques [7]; understanding the implementation choices made during the system
design is important because this provides insights that can assist the
development of new algorithms. Index Terms - Speech enhancement,
dereverberation, denoising, Kalman filter, minimum mean squared error
estimation.Comment: 13 page
Modulation-Domain Kalman Filtering for Monaural Blind Speech Denoising and Dereverberation
We describe a monaural speech enhancement algorithm based on
modulation-domain Kalman filtering to blindly track the time-frequency
log-magnitude spectra of speech and reverberation. We propose an adaptive
algorithm that performs blind joint denoising and dereverberation, while
accounting for the inter-frame speech dynamics, by estimating the posterior
distribution of the speech log-magnitude spectrum given the log-magnitude
spectrum of the noisy reverberant speech. The Kalman filter update step models
the non-linear relations between the speech, noise and reverberation
log-spectra. The Kalman filtering algorithm uses a signal model that takes into
account the reverberation parameters of the reverberation time, , and
the direct-to-reverberant energy ratio (DRR) and also estimates and tracks the
and the DRR in every frequency bin in order to improve the estimation
of the speech log-magnitude spectrum. The Kalman filtering algorithm is tested
and graphs that depict the estimated reverberation features over time are
examined. The proposed algorithm is evaluated in terms of speech quality,
speech intelligibility and dereverberation performance for a range of
reverberation parameters and SNRs, in different noise types, and is also
compared to competing denoising and dereverberation techniques. Experimental
results using noisy reverberant speech demonstrate the effectiveness of the
enhancement algorithm.Comment: 13 pages, 13 figures, Submitted to IEEE Transactions on Audio, Speech
and Language Processin