Noise reduction

About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.

Binaural signal processing using multiple acoustic sensors and digital filtering

Abstract: A desired acoustic signal is extracted from a noisy environment by generating a signal representative of the desired signal with processor ( 30 ). Processor ( 30 ) receives aural signals from two sensors ( 22, 24 ) each at a different location. The two inputs to processor ( 30 ) are converted from analog to digital format and then submitted to a discrete Fourier transform process to generate discrete spectral signal representations. The spectral signals are delayed to provide a number of intermediate signals, each corresponding to a different spatial location relative to the two sensors. Locations of the noise source and the desired source, and the spectral content of the desired signal are determined from the intermediate signal corresponding to the noise source locations. Inverse transformation of the selected intermediate signal followed by digital to analog conversion provides an output signal representative of the desired signal with output device ( 90 ). Techniques to localize multiple acoustic sources are also disclosed. Further, a technique to enhance noise reduction from multiple sources based on two-sensor reception is described.

Musical-Noise-Free Speech Enhancement Based on Optimized Iterative Spectral Subtraction

Abstract: In this paper, we provide a theoretical analysis of the amount of musical noise in iterative spectral subtraction, and its optimization method for the least musical noise generation. To achieve high-quality noise reduction with low musical noise, iterative spectral subtraction, i.e., iteratively applied weak nonlinear signal processing, has been proposed. Although the effectiveness of the method has been reported experimentally, there have been no theoretical studies. Therefore, in this paper, we formulate the generation process of musical noise by tracing the change in kurtosis of noise spectra, and conduct a comparison of the amount of musical noise for different parameter settings but the same achieved level of noise attenuation. Furthermore, we theoretically derive the optimal internal parameters that generate no musical noise. It is clarified that to find a fixed point in kurtosis yields the no-musical-noise property. Comparative experiments with commonly used noise reduction methods show the proposed method's efficacy.

On-line detection and measurement of partial discharge signals in a noisy environment

Abstract: In extracting partial discharge (PD) signals embedded in excessive noise, the need for an online and automated tool becomes a crucial necessity One of the recent approaches that have gained some acceptance within the research arena is the Wavelet multi- resolution analysis (WMRA) However selecting an accurate mother wavelet, defining dynamic threshold values and identifying the resolution levels to be considered in the PD extraction from the noise are still challenging tasks This paper proposes a novel wavelet-based technique for extracting PD signals embedded in high noise levels The proposed technique enhances the WMRA by decomposing the noisy data into different resolution levels while sliding it into Kaiser's window Only the maximum expansion coefficients at each resolution level are used in de-noising and measuring the extracted PD signal A small set of coefficients is used in the monitoring process without assigning threshold values or performing signal reconstruction The proposed monitoring technique has been applied to a laboratory data as well as to a simulated PD pulses embedded in a collected laboratory noise

Robust Speech Recognition Using a Cepstral Minimum-Mean-Square-Error-Motivated Noise Suppressor

Abstract: We present an efficient and effective nonlinear feature-domain noise suppression algorithm, motivated by the minimum-mean-square-error (MMSE) optimization criterion, for noise-robust speech recognition. Distinguishing from the log-MMSE spectral amplitude noise suppressor proposed by Ephraim and Malah (E&M), our new algorithm is aimed to minimize the error expressed explicitly for the Mel-frequency cepstra instead of discrete Fourier transform (DFT) spectra, and it operates on the Mel-frequency filter bank's output. As a consequence, the statistics used to estimate the suppression factor become vastly different from those used in the E&M log-MMSE suppressor. Our algorithm is significantly more efficient than the E&M's log-MMSE suppressor since the number of the channels in the Mel-frequency filter bank is much smaller (23 in our case) than the number of bins (256) in DFT. We have conducted extensive speech recognition experiments on the standard Aurora-3 task. The experimental results demonstrate a reduction of the recognition word error rate by 48% over the standard ICSLP02 baseline, 26% over the cepstral mean normalization baseline, and 13% over the popular E&M's log-MMSE noise suppressor. The experiments also show that our new algorithm performs slightly better than the ETSI advanced front end (AFE) on the well-matched and mid-mismatched settings, and has 8% and 10% fewer errors than our earlier SPLICE (stereo-based piecewise linear compensation for environments) system on these settings, respectively.