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Section: New Results
Single-Channel Audio Processing
While most of our audio scene analysis work involves microphone arrays, it is important to develop single-channel (one microphone) signal processing methods as well. In particular, it is important to detect speech signal (or voice) in the presence of various types of noise (stationary or non-stationary). In this context, we developed the following methods [39], [37]:
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Statistical likelihood ratio test is a widely used voice activity detection (VAD) method, in which the likelihood ratio of the current temporal frame is compared with a threshold. A fixed threshold is always used, but this is not suitable for various types of noise. In this work, an adaptive threshold is proposed as a function of the local statistics of the likelihood ratio. This threshold represents the upper bound of the likelihood ratio for the non-speech frames, whereas it remains generally lower than the likelihood ratio for the speech frames. As a result, a high non-speech hit rate can be achieved, while maintaining speech hit rate as large as possible.
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Estimating the noise power spectral density (PSD) is essential for single channel speech enhancement algorithms. We propose a noise PSD estimation approach based on regional statistics which consist of four features representing the statistics of the past and present periodograms in a short-time period. We show that these features are efficient in characterizing the statistical difference between noise PSD and noisy-speech PSD. We therefore propose to use these features for estimating the speech presence probability (SPP). The noise PSD is recursively estimated by averaging past spectral power values with a time-varying smoothing parameter controlled by the SPP. The proposed method exhibits good tracking capability for non-stationary noise, even for abruptly increasing noise level.
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