Noise PSD estimation by logarithmic baseline tracing

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 parameterβ 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.

Speech-codebook based soft Voice Activity Detection

“…Therefore, a baseline tracing of the noise floor b f (λ) and ceiling bc(λ) is employed. This method is similar to noise estimation known from speech enhancement [16,17]. The noise floor tracing is implemented according to:…”