Voicing detection based on adaptive aperiodicity thresholding for speech enhancement in non‐stationary noise

Abstract

In this study, the authors present a novel voicing detection algorithm which employs the well-known aperiodicity measure to detect voiced speech in signals contaminated with non-stationary noise. The method computes a signal-adaptive decision threshold which takes into account the current noise level, enabling voicing detection by direct comparison with the extracted aperiodicity. This adaptive threshold is updated at each frame by making a simple estimate of the current noise power, and thus is adapted to fluctuating noise conditions. Once the aperiodicity is computed, the method only requires a small number of operations, and enables its implementation in challenging devices (such as hearing aids) if an efficient approximation of the difference function is employed to extract the aperiodicity. Evaluation over a database of speech sentences degraded by several types of noise reveals that the proposed voicing classifier is robust against different noises and signal-to-noise ratios. In addition, to evaluate the applicability of the method for speech enhancement, a simple F 0-based speech enhancement algorithm integrating the proposed classifier is implemented. The system is shown to achieve competitive results, in terms of objective measures, when compared with other well-known speech enhancement approaches.

Reconstruction of uniformly sampled signals from non‐uniform short samples in fractional Fourier domain

“…Non‐stationary signal processing, such as the processing of gravity waves, broad‐band chirp signals and radar signals, plays a fundamental role in the area of modern digital signal processing [1, 2]. As the classical Fourier transform (FT) is not suitable for analysing and processing non‐stationary signals, many useful tools have been introduced, such as the short‐time Fourier transform, wavelet transform, fractional FT (FRFT) and linear canonical transform (LCT) [3–7].…”