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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.

Abstract-In embedded systems using fixed-point arithmetic, converting applications into fixed-point representations requires a fast and efficient accuracy evaluation. This paper presents a new analytical approach to determine an estimation of the numerical accuracy of a fixed-point system, which is accurate and valid for all systems formulated with smooth operations (e.g. additions, subtractions, multiplications and divisions). The mathematical expression of the system output noise power is determined using matrices to obtain more compact expressions. The proposed approach is based on the determination of the timevarying impulse-response of the system. To speedup computation of the expressions, the impulse response is modelled using a linear prediction approach. The approach is illustrated in the general case of time-varying recursive systems by the Least Mean Square (LMS) algorithm example. Experiments on various and representative applications show the fixedpoint accuracy estimation quality of the proposed approach. Moreover, the approach using the linear-prediction approximation is very fast even for recursive systems. A significant speed-up compared to the best known accuracy evaluation approaches is measured even for the most complex benchmarks.

This paper addresses the problem of single microphone speech enhancement in noisy environments. Common short-time noise reduction techniques proposed in the art are expressed as a spectral gain depending on the a priori SNR. In the well-known decisiondirected approach, the a priori SNR depends on the speech spectrum estimation in the previous frame. As a consequence the gain function matches the previous frame rather than the current one which degrades the noise reduction performance. We propose a new method called Two-Step Noise Reduction (TSNR) technique which solves this problem while maintaining the benefits of the decision-directed approach. This method is analyzed and results in voice communication and speech recognition context are given.

This paper addresses the problem of single microphone speech enhancement in noisy environments. Common short-time noise reduction techniques introduce harmonic distortion in enhanced speech because of the non reliability of estimators for small signalto-noise ratios. We propose a new method called Harmonic Regeneration Noise Reduction technique which solves this problem. A fully harmonic signal is calculated based on the distorted signal using a non-linearity to regenerate harmonics in an efficient way. This artificial signal is then used to compute a suppression gain able to preserve the speech harmonics. This method is theoretically analyzed, then objective and formal subjective results are given and show a significant improvement compared to classical noise reduction techniques.