scholarly journals Noise Reduction in Car Speech

10.14311/1111 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
V. Bolom
Keyword(s):  
Noise Reduction ◽  
Speech Enhancement ◽  
Mixed Model ◽  
Noise Suppression ◽  
Speech Signals ◽  
Noisy Environment ◽  
Free Communication ◽  
Speech Distortion ◽  
Criteria For Evaluation

This paper presents properties of chosen multichannel algorithms for speech enhancement in a noisy environment. These methods are suitable for hands-free communication in a car cabin. Criteria for evaluation of these systems are also presented. The criteria consider both the level of noise suppression and the level of speech distortion. The performance of multichannel algorithms is investigated for a mixed model of speech signals and car noise and for real signals recorded in a car. 

scholarly journals Dual-Channel Speech Enhancement Based on Speech Presence Probability

2020 ◽  
Vol 9 (3) ◽  
pp. 2198-2200
Keyword(s):  
Noise Reduction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Speech Processing ◽  
Speech Signal ◽  
Reduction Process ◽  
Coherent Noise ◽  
Residual Noise ◽  
Speech Distortion ◽  
Reduction Methods

This paper introduces technology to improve sound quality, which serves the needs of media and entertainment. Major challenging problem in the speech processing applications like mobile phones, hands-free phones, car communication, teleconference systems, hearing aids, voice coders, automatic speech recognition and forensics etc., is to eliminate the background noise. Speech enhancement algorithms are widely used for these applications in order to remove the noise from degraded speech in the noisy environment. Hence, the conventional noise reduction methods introduce more residual noise and speech distortion. So, it has been found that the noise reduction process is more effective to improve the speech quality but it affects the intelligibility of the clean speech signal. In this paper, we introduce a new model of coherence-based noise reduction method for the complex noise environment in which a target speech coexists with a coherent noise around. From the coherence model, the information of speech presence probability is added to better track noise variation accurately; and during the speech presence and speech absent period, adaptive coherence-based method is adjusted. The performance of suggested method is evaluated in condition of diffuse and real street noise, and it improves the speech signal quality less speech distortion and residual noise.

scholarly journals Application of Perceptual Filtering Models to Noisy Speech Signals Enhancement

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Novlene Zoghlami ◽  
Zied Lachiri
Keyword(s):  
Speech Enhancement ◽  
Noise Suppression ◽  
Filter Banks ◽  
Perceptual Quality ◽  
Speech Signals ◽  
Subjective Test ◽  
Noisy Speech ◽  
Estimation Algorithms ◽  
Noise Estimate ◽  
Quality Rating

This paper describes a new speech enhancement approach using perceptually based noise reduction. The proposed approach is based on the application of two perceptual filtering models to noisy speech signals: the gammatone and the gammachirp filter banks with nonlinear resolution according to the equivalent rectangular bandwidth (ERB) scale. The perceptual filtering gives a number of subbands that are individually spectral weighted and modified according to two different noise suppression rules. The importance of an accurate noise estimate is related to the reduction of the musical noise artifacts in the processed speech that appears after classic subtractive process. In this context, we use continuous noise estimation algorithms. The performance of the proposed approach is evaluated on speech signals corrupted by real-world noises. Using objective tests based on the perceptual quality PESQ score and the quality rating of signal distortion (SIG), noise distortion (BAK) and overall quality (OVRL), and subjective test based on the quality rating of automatic speech recognition (ASR), we demonstrate that our speech enhancement approach using filter banks modeling the human auditory system outperforms the conventional spectral modification algorithms to improve quality and intelligibility of the enhanced speech signal.

scholarly journals Deep Learning-Based Speech Enhancement With a Loss Trading Off the Speech Distortion and the Noise Residue for Cochlear Implants

2021 ◽  
Vol 8 ◽  
Author(s):  
Yuyong Kang ◽  
Nengheng Zheng ◽  
Qinglin Meng
Keyword(s):  
Deep Learning ◽  
Speech Perception ◽  
Speech Enhancement ◽  
Noise Suppression ◽  
Common Knowledge ◽  
Acoustic Vibration ◽  
Auditory Prosthesis ◽  
Cochlear Hair Cells ◽  
Time Frequency ◽  
Speech Distortion

The cochlea plays a key role in the transmission from acoustic vibration to neural stimulation upon which the brain perceives the sound. A cochlear implant (CI) is an auditory prosthesis to replace the damaged cochlear hair cells to achieve acoustic-to-neural conversion. However, the CI is a very coarse bionic imitation of the normal cochlea. The highly resolved time-frequency-intensity information transmitted by the normal cochlea, which is vital to high-quality auditory perception such as speech perception in challenging environments, cannot be guaranteed by CIs. Although CI recipients with state-of-the-art commercial CI devices achieve good speech perception in quiet backgrounds, they usually suffer from poor speech perception in noisy environments. Therefore, noise suppression or speech enhancement (SE) is one of the most important technologies for CI. In this study, we introduce recent progress in deep learning (DL), mostly neural networks (NN)-based SE front ends to CI, and discuss how the hearing properties of the CI recipients could be utilized to optimize the DL-based SE. In particular, different loss functions are introduced to supervise the NN training, and a set of objective and subjective experiments is presented. Results verify that the CI recipients are more sensitive to the residual noise than the SE-induced speech distortion, which has been common knowledge in CI research. Furthermore, speech reception threshold (SRT) in noise tests demonstrates that the intelligibility of the denoised speech can be significantly improved when the NN is trained with a loss function bias to more noise suppression than that with equal attention on noise residue and speech distortion.

Far Field Noise Suppression Method in McWiLL Intercom Based on Double Uni-Directional Microphone

2011 ◽  
Vol 267 ◽  
pp. 104-108
Author(s):  
Yu Hua Zhang ◽  
Li Min Jia ◽  
Zhong Li
Keyword(s):  
Noise Reduction ◽  
Noise Suppression ◽  
Test Method ◽  
Far Field ◽  
Noisy Environment ◽  
Directional Microphone ◽  
Noise Cancelling ◽  
Suppression Method ◽  
Field Noise

To satisfy McWiLL communication requirement in noisy environment, a far field noise suppression method based on double uni-direction microphone in McWiLL intercom was studied. The method arranges two uni-direction microphones rationally and uses analog noise cancelling processor to accomplish surrounding noise reduction in McWiLL intercom in noisy environment. To verify validity of the method, several contrast experiments using diagnostic rhyme test method were done. Experiments results show that the far field noise suppression method based on double uni-direction microphone is effective for surrounding noise reduction.

Speech Intelligibility Enhancement Using Distortion Control

2014 ◽  
Vol 912-914 ◽  
pp. 1391-1394
Author(s):  
Yu Xiang Yang ◽  
Jian Fen Ma
Keyword(s):  
Noise Reduction ◽  
Speech Enhancement ◽  
Speech Intelligibility ◽  
Signal Distortion ◽  
Gain Function ◽  
Noisy Speech ◽  
Speech Distortion ◽  
Distortion Control ◽  
Enhancement Algorithm

In order to improve the intelligibility of the noisy speech, a novel speech enhancement algorithm using distortion control is proposed. The reason why current speech enhancement algorithm cannot improve speech intelligibility is that these algorithms aim to minimize the overall distortion of the enhanced speech. However, different speech distortions make different contributions to the speech intelligibility. The distortion in excess of 6.02dB has the most detrimental effects on speech intelligibility. In the process of noise reduction, the type of speech distortion can be determined by signal distortion ratio. The distortion in excess of 6.02dB can be properly controlled via tuning the gain function of the speech enhancement algorithm. The experiment results show that the proposed algorithm can improve the intelligibility of the noisy speech considerably.

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