Noise PSD Estimation By Logarithmic Baseline Tracing

Authors:

Heese, F. ,  Vary, P.

Book Title:

Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

Organization:

IEEE

Pages:

p.p. 4405 - 4409

Date:

Apr. 2015

DOI:

10.1109/ICASSP.2015.7178803

Language:

English

Abstract

A novel noise power spectral density (PSD) estimator for disturbed speech signals which operates in the short-time ourier domain is presented. A noise PSD estimate is provided y constrained tracing with time of the noisy observation separately for each frequency bin. The constraint is a limitation of the logarithmic magnitude change between successive time frames. Since speech onset is assumed as sudden rises in the noisy observation, a fixed and adaptive tracing parameter beta has been derived to track the contained noise while preventing speech leakage to the noise PSD estimate. The experimental evaluation and comparison with state-of-the-art algorithms, SPP and Minimum Statistics, confirms a lower logarithmic noise estimation error and superior speech enhancement rated in a standard noise reduction system. The proposed concept has extremely low computational complexity and memory usage. Thus, it is well suited for applications where processing power and memory is limited.

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