NOISE PSD ESTIMATION BY LOGARITHMIC BASELINE TRACING

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 parameterβ has been derived to track the contained noise while pre-venting 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 compu-tational complexity and memory usage. Thus, it is well suited for applications where processing power and memory is limited. Index Terms — Noise power estimation, speech enhancement, noise reduction, low complexity, low memory 1