Modulation-domain Kalman filtering for single-channel speech enhancement

Authors:
Stephen So;Kuldip K. Paliwal
Affiliations:
Signal Processing Laboratory, Griffith School of Engineering, Griffith University, Brisbane, QLD 4111, Australia;Signal Processing Laboratory, Griffith School of Engineering, Griffith University, Brisbane, QLD 4111, Australia
Venue:
Speech Communication
Year:
2011

Citing 9
Cited 1

Kalman Filtering for Low Distortion Speech Enhancement in Mobile Communication

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Joint acoustic and modulation frequency

EURASIP Journal on Applied Signal Processing
Discrete-time speech signal processing: principles and practice

Discrete-time speech signal processing: principles and practice
Kalman fitler with phase spectrum compensation algorithm for speech enhancement

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Single-channel speech enhancement using spectral subtraction in the short-time modulation domain

Speech Communication
Suppressing the influence of additive noise on the Kalman gain for low residual noise speech enhancement

Speech Communication
Role of modulation magnitude and phase spectrum towards speech intelligibility

Speech Communication
Filtering of colored noise for speech enhancement and coding

IEEE Transactions on Signal Processing
New insights into the noise reduction Wiener filter

IEEE Transactions on Audio, Speech, and Language Processing

Speech enhancement using a minimum mean-square error short-time spectral modulation magnitude estimator

Speech Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we investigate the modulation-domain Kalman filter (MDKF) and compare its performance with other time-domain and acoustic-domain speech enhancement methods. In contrast to previously reported modulation domain-enhancement methods based on fixed bandpass filtering, the MDKF is an adaptive and linear MMSE estimator that uses models of the temporal changes of the magnitude spectrum for both speech and noise. Also, because the Kalman filter is a joint magnitude and phase spectrum estimator, under non-stationarity assumptions, it is highly suited for modulation-domain processing, as phase information has been shown to play an important role in the modulation domain. We have found that the Kalman filter is better suited for processing in the modulation-domain, rather than in the time-domain, since the low order linear predictor is sufficient at modelling the dynamics of slow changes in the modulation domain, while being insufficient at modelling the long-term correlation speech information in the time domain. As a result, the MDKF method produces enhanced speech that has very minimal distortion and residual noise, in the ideal case. The results from objective experiments and blind subjective listening tests using the NOIZEUS corpus show that the MDKF (with clean speech parameters) outperforms all the acoustic and time-domain enhancement methods that were evaluated, including the time-domain Kalman filter with clean speech parameters. A practical MDKF that uses the MMSE-STSA method to enhance noisy speech in the acoustic domain prior to LPC analysis was also evaluated and showed promising results.