Improved noise power spectral density tracking by a MAP-based postprocessor
- Resource Type
- Conference
- Authors
- Chinaev, Aleksej; Krueger, Alexander; Tran Vu, Dang Hai; Haeb-Umbach, Reinhold
- Source
- 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. :4041-4044 Mar, 2012
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Speech
Speech enhancement
Noise measurement
Estimation
Frequency estimation
Signal to noise ratio
Noise power estimation
MAP parameter estimation
speech enhancement
- Language
- ISSN
- 1520-6149
2379-190X
In this paper we present a novel noise power spectral density tracking algorithm and its use in single-channel speech enhancement. It has the unique feature that it is able to track the noise statistics even if speech is dominant in a given time-frequency bin. As a consequence it can follow non-stationary noise superposed by speech, even in the critical case of rising noise power. The algorithm requires an initial estimate of the power spectrum of speech and is thus meant to be used as a postprocessor to a first speech enhancement stage. An experimental comparison with a state-of-the-art noise tracking algorithm demonstrates lower estimation errors under low SNR conditions and smaller fluctuations of the estimated values, resulting in improved speech quality as measured by PESQ scores.