Filtering Techniques for Noise Reduction and Speech Enhancement

The performance of speech enhancement algorithms can be further improved by considering the application scenarios of speech products. In this paper, we propose an attention-based branchy neural network framework by incorporating the prior environmental information for noise reduction. In the whole denoising framework, first, an environment classification network is trained to distinguish the noise type of each noisy speech frame. Guided by this classification network, the denoising network gradually learns respective noise reduction abilities in different branches. Unlike most deep neural network (DNN)-based methods, which learn speech reconstruction capabilities with a common neural structure from all training noises, the proposed branchy model obtains greater performance benefits from the specially trained branches of prior known noise interference types. Experimental results show that the proposed branchy DNN model not only preserved better enhanced speech quality and intelligibility in seen noisy environments, but also obtained good generalization in unseen noisy environments.

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Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises

Applied Sciences ◽

10.3390/app10176077 ◽

2020 ◽

Vol 10 (17) ◽

pp. 6077

Author(s):

Gyuseok Park ◽

Woohyeong Cho ◽

Kyu-Sung Kim ◽

Sangmin Lee

Keyword(s):

Neural Networks ◽

Noise Reduction ◽

Speech Enhancement ◽

Hearing Aids ◽

Deep Neural Networks ◽

Hearing Aid ◽

Environmental Noise ◽

Speech Quality ◽

Impaired Hearing ◽

Noise Classification

Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.

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Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

Advances in Meteorology ◽

10.1155/2014/517571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Soojun Kim ◽

Huiseong Noh ◽

Narae Kang ◽

Keonhaeng Lee ◽

Yonsoo Kim ◽

...

Keyword(s):

Noise Reduction ◽

Correlation Coefficient ◽

Standard Error ◽

Rainfall Series ◽

Radar Rainfall ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass ◽

Filtering Technique ◽

Filtering Techniques

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.

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