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.

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.

Conventional adaptive array antenna processing must observe signals on all of the array antenna elements. However, because the low-cost electronically steerable parasitic array radiator (ESPAR) antenna has only a single-port output, none of the signals on the antenna's parasitic elements can be observed. A direct application of most of the algorithms for the conventional adaptive array antenna is impractical. In this paper, a new technique of estimation of direction-of-arrivals (DoAs) is proposed for the ESPAR antenna. This technique is based on the modified MUltiple SIgnal Classification (MUSIC) algorithm. The correlation matrix used in the MUSIC algorithm is estimated from the signal received through the single-port output of the ESPAR antenna as it switches over a set of antenna patterns. Simulation results show that DoAs can be estimated by the reactance domain MUSIC algorithm for ESPAR antennas. Furthermore, the statistical performance on estimation error variance of the reactance domain MUSIC estimator is analyzed and compared with the Cramér-Rao lower bound. Analytic and empirical results show that high-resolution DoAs estimation can be achieved by using the reactance domain MUSIC algorithm for ESPAR antennas.Index Terms-Direction finder, electronically steerable parasitic array radiator (ESPAR) antenna, reactance domain MUltiple SIgnal Classification (MUSIC) algorithm.

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.

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