Publications – Details
Noise PSD Estimation By Logarithmic Baseline Tracing
- Authors:
- Florian Heese and Peter Vary
- Book Title:
- Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
- Venue:
- Brisbane, QLD, Australia
- Event Date:
- 19.-24.4.2015
- Organization:
- IEEE
- Location:
- Piscataway, NJ, USA
- Date:
- Apr. 2015
- Pages:
- 4405–4409
- URL:
- 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 Fourier domain is
presented. A noise PSD estimate is provided by 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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