Noise Tracking Using DFT Domain Subspace Decompositions

Noise Tracking Using DFT Domain Subspace Decompositions

Hendriks, R.C.
Jensen, J.
Heusdens, R.

Electrical Engineering, Mathematics and Computer Science

Mediamatics

2008-03-01

All discrete Fourier transform (DFT) domain-based speech enhancement gain functions rely on knowledge of the noise power spectral density (PSD). Since the noise PSD is unknown in advance, estimation from the noisy speech signal is necessary. An overestimation of the noise PSD will lead to a loss in speech quality, while an underestimation will lead to an unnecessary high level of residual noise. We present a novel approach for noise tracking, which updates the noise PSD for each DFT coefficient in the presence of both speech and noise. This method is based on the eigenvalue decomposition of correlation matrices that are constructed from time series of noisy DFT coefficients. The presented method is very well capable of tracking gradually changing noise types. In comparison to state-of-the-art noise tracking algorithms the proposed method reduces the estimation error between the estimated and the true noise PSD. In combination with an enhancement system the proposed method improves the segmental SNR with several decibels for gradually changing noise types. Listening experiments show that the proposed system is preferred over the state-of-the-art noise tracking algorithm.

Discrete Fourier transform (DFT) domain subspace
decompositions
noise tracking
speech enhancement

http://resolver.tudelft.nl/uuid:869337e6-b5a5-476c-962a-8fce2110c7bb

IEEE

1558-7916

IEEE Transactions on Audio, Speech, and Language Processing, 16 (3), 2008

Institutional Repository

journal article

(c) 2008 IEEE