CAPPI? ELIMINATION OF THE MUSICAL NOISE PHENOMEMON Elimination of the Musical Noise Phenomenon with the Ephraim and Malah Noise Suppressor

Abstract-This paper presents a study of the noise suppression technique proposed by Ephraim and Malah. This technique has been used recently for the restoration of degraded audio recordings because it is free of the frequently encountered 'musical noise' artifact. It is demonstrated how this artifact is actually eliminated without bringing distortion to the recorded signal even if the noise is only poorly stationary

A comparison of soft and hard thresholding by using discrete wavelet transforms

This paper  about to reduce the  noise by Adaptive time-frequency Block Thresholding procedure using discrete wavelet transform to achieve better SNR of the audio signal. .  Discrete-wavelet transforms based algorithms are used for audio signal denoising. The resulting algorithm is robust to variations of signal structures such as short transients and long harmonics.  Analysis is done on noisy speech signal corrupted by white noise at 0dB, 5dB, 10dB and 15dB signal to noise ratio levels. Here both hard thresholding and soft thresholding are used for denoising. Simulation & results are performed in MATLAB 7.10.0 (R2010a).  In this paper we are comparing results of soft thresholding and hard thresholding

Reducción de ruido en la detección automática de hipernasalidad en niños

RESUMEN: En este artículo se presenta una metodología para reducir el ruido de fondo en un sistema de detección de hipernasalidad; se utilizan algunas medidas clásicas de calidad e inteligibilidad para evaluar los algoritmos, que mejoran las señales de voz, utilizados en el sistema. La detección de hipernasalidad se realiza con un clasificador lineal y se comparan los resultados obtenidos con diferentes algoritmos de sustracción espectral. Los resultados muestran que las técnicas de sustracción espectral pueden ser usadas para mejorar el rendimiento del clasificador en la detección de hipernasalidad cuando las señales se encuentran contaminadas con ruido aditivo.ABSTRACT: In this paper a methodology to reduce the background noise in a hypernasality detector system using spectral subtraction method is presented, some classical measures of quality and intelligibility are used to evaluate the speech enhancements algorithms used in the system. A linear classifier is used for the hypernasality detection and the results obtained with different spectral subtraction algorithms are compared. The results show that the spectral subtraction techniques can be used to improve the performance of the classifier in the detection of hypernasality when signals are contaminated with additive noise

A Family of Coherence-Based Multi-Microphone Speech Enhancement Systems

This contribution addresses the problem of additive noise reduction in speech picked up by a microphone in a noisy environment. Two systems belonging to the family of coherence-based noise cancellers are presented. Suggested systems have the modular structure using 2 or 4 microphones and suppress non-stationary noises in the range of 4 to 17 dB depending on the chosen structure and noise characteristics. The common properties are acceptable noise suppression, low speech distortion and residual noise

Studies in Signal Processing Techniques for Speech Enhancement: A comparative study

Speech enhancement is very essential to suppress the background noise and to increase speech intelligibility and reduce fatigue in hearing. There exist many simple speech enhancement algorithms like spectral subtraction to complex algorithms like Bayesian Magnitude estimators based on Minimum Mean Square Error (MMSE) and its variants. A continuous research is going and new algorithms are emerging to enhance speech signal recorded in the background of environment such as industries, vehicles and aircraft cockpit. In aviation industries speech enhancement plays a vital role to bring crucial information from pilot’s conversation in case of an incident or accident by suppressing engine and other cockpit instrument noises. In this work proposed is a new approach to speech enhancement making use harmonic wavelet transform and Bayesian estimators. The performance indicators, SNR and listening confirms to the fact that newly modified algorithms using harmonic wavelet transform indeed show better results than currently existing methods. Further, the Harmonic Wavelet Transform is computationally efficient and simple to implement due to its inbuilt decimation-interpolation operations compared to those of filter-bank approach to realize sub-bands

A Robust Noise Spectral Estimation Algorithm for Speech Enhancement in Voice Devices

In this thesis, a new robust noise spectral estimation algorithm is proposed for the purpose of single-microphone speech enhancement. This algorithm can generate the optimal noise spectral estimates in the Minimum Mean Square Error (MMSE) sense based on the speech statistics in the noisy environments. Compared to the well-adopted conventional noise spectral estimation method using the single-pole recursion, our proposed scheme is more reliable since the recursion coefficients are adaptable and optimal in the MMSE therein. We also propose a new accurate Resulting Signal-to-Noise Ratio (R-SNR) estimator as a quality measure to benchmark the existing noise spectral estimation techniques. This new R-SNR estimator can be applied to quantify not only the residual noise but also the speech distortion and therefore it can well serve as the overall speech quality measure after the noise suppression. We conduct the experiments to evaluate the performance of the noise suppression using our robust noise spectral estimation algorithm and compare it with those of two major existing noise spectral estimation methods. Through numerous simulations, we have shown that our noise suppression technique significantly outperforms the conventional methods in both stationary and nonstationary noise environments