Improved Signal-to-Noise Ratio Estimation for Speech Enhancement

Abstract

This paper addresses the problem of single microphone speech enhancement in noisy environments. State-ofthe-art short-time noise reduction techniques are most often expressed as a spectral gain depending on the Signal-to-Noise Ratio (SNR). The well-known decision-directed (DD) approach drastically limits the level of musical noise but the estimated a priori SNR is biased since it depends on the speech spectrum estimation in the previous frame. Therefore the gain function matches the previous frame rather than the current one which degrades the noise reduction performance. The consequence of this bias is an annoying reverberation effect. We propose a method called Two-Step Noise Reduction (TSNR) technique which solves this problem while maintaining the benefits of the decision-directed approach. The estimation of the a priori SNR is refined by a second step to remove the bias of the DD approach, thus removing the reverberation effect. However, classic short-time noise reduction techniques, including TSNR, introduce harmonic distortion in enhanced speech because of the unreliability of estimators for small signal-tonoise ratios. This is mainly due to the difficult task of noise PSD estimation in single microphone schemes. To overcome this problem, we propose a method called Harmonic Regeneration Noise Reduction (HRNR). A non-linearity is used to regenerate the degraded harmonics of the distorted signal in an efficient way. The resulting artificial signal is produced in order to refine the a priori SNR used to compute a spectral gain able to preserve the speech harmonics. These methods are analyzed and objective and formal subjective test results between HRNR and TSNR techniques are provided. A significant improvement is brought by HRNR compared to TSNR thanks to the preservation of harmonics.

Speech Denoising in White Noise Based on Signal Subspace Low-rank Plus Sparse Decomposition

“…The typical algorithms including spectral subtraction [1], minimum mean square error (MMSE) estimation [2][3][4], Wiener filtering [5][6][7][8], and subspace methods [9][10][11][12][13]. Spectral Subtraction and Wiener filtering have been widely used for enhancing speech because of their simplicity and ease of implementation in single channel systems but they suffer from the production of musical noise after enhancement and is one of their major drawbacks.…”

Объективное Оценивание Качества Алгоритмов Радикального Шумоподавления

“…В 2004-2006 предложены два но-вых алгоритма шумоподавления: алгоритм двухэтапного шумоподавления Wiener-TSNR (Wiener two-step noise reduction) и алгоритм шумоподавления с регенерацией гармоник Wiener-HRNR (Wiener harmonic regeneration noise reduction) [5,6]. Эти алгоритмы обеспе-чивают очень низкий уровень остаточного шума, что позволяет отнести их к классу алго-ритмов радикального шумоподавления.…”

“…Так, в [5] по-казано, что при использовании алгоритма Wiener-TSNR в системе АРР, в условиях уме-ренной согласованности данных в режимах обучения и тестирования, удалось на 9% пони-зить количество ошибок распознавания типа «замена» и на 22% понизить количество оши-бок распознавания типа «вставка». В [6] приве-дены результаты оценивания качества новых алгоритмов с использованием объективных…”

Modulation frequency domain adaptive gain equalizer using convex optimization

“…There have also been some efforts to reduce this musical noise such as [4] but this improvement has the tendency of producing audibledistortion causing listening discomfort even compared to the unprocessed signal [5]. Reducing noise without generating artifacts was proposed in [6] but this method fails to address unvoiced speech.…”