Improved Signal-to-Noise Ratio Estimation for Speech Enhancement

• Published in: IEEE transactions on audio, speech, and language processing Vol. 14; no. 6; pp. 2098 - 2108
• Main Authors: Plapous, C., Marro, C., Scalart, P.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01-11-2006; Institute of Electrical and Electronics Engineers
• Subjects: A posteriori signal-to-noise ratio (SNR); a priori SNR; Applied sciences; Bias; Computer Science; Degradation; Detection, estimation, filtering, equalization, prediction; Engineering Sciences; Exact sciences and technology; Gain; Harmonic distortion; harmonic regeneration; Harmonics; Information, signal and communications theory; Miscellaneous; Noise level; Noise reduction; Performance gain; Reverberation; Signal and communications theory; Signal and Image Processing; Signal processing; Signal representation. Spectral analysis; Signal to noise ratio; Signal, noise; Spectra; Spectral analysis; Speech; Speech enhancement; Speech processing; Telecommunications and information theory; Working environment noise; Performance evaluation; State of the art; Microphone; Noise reduction; Small signal behavior; Signal estimation; Background noise; Non linear phenomenon; Noise level; Noise spectrum; Spectrum analysis; Speech enhancement; Harmonic distortion; harmonic regeneration; Small signal; Power spectral density; Step method; Harmonics suppression; A posteriori signal-to-noise ratio (SNR); A priori estimation; Signal processing; a priori SNR; Non linear effect; Signal analysis; Speech processing; Signal to noise ratio
• ISSN: 1558-7916; 1558-7924
• DOI: 10.1109/TASL.2006.872621

This paper addresses the problem of single-microphone speech enhancement in noisy environments. State-of-the-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-to-noise ratios. This is mainly due to the difficult task of noise power spectrum density (PSD) estimation in single-microphone schemes. To overcome this problem, we propose a method called harmonic regeneration noise reduction (HRNR). A nonlinearity 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