An RNN-based Speech Enhancement Method for a Binaural Hearing Aid System

Laser speech measurement is a new sound capture technology based on Laser Doppler Vibrometry (LDV). It avoids the need for contact, is easily concealed and is ideal for remote speech acquisition, which has led to its wide-scale adoption for military and security applications. However, lasers are easily affected by complex detection environments. Thus, speckle noise often appears in the measured speech, seriously affecting its quality and intelligibility. This paper examines all of the characteristics of impulsive noise in laser measured speech and proposes a novel automatic impulsive noise detection and removal method. This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the noise-to-signal ratio (NSR) of the measured signal. This makes it possible to detect the position of noise through a combination of the average short-time energy and kurtosis. The method not only precisely locates small clicks (with a duration of just a few samples), but also finds the location of longer bursts and scratches (with a duration of up to a hundred samples). The located samples can then be replaced by more appropriate samples whose coding is based on the LP model. This strategy avoids unnecessary processing and obviates the need to compromise the quality of the relatively large fraction of samples that are unaffected by speckle noise. Experimental results show that the proposed automatic speckle noise detection and removal method outperforms other related methods across a wide range of degraded audio signals.

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“…Recently, some data driven methods have been proposed to suppress noise [21,22]. However, they all focus on background noise.…”

Section: Related Workmentioning

confidence: 99%

Speckle Noise Detection and Removal for Laser Speech Measurement Systems

2021

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“…Binaural signals will be trained separately after being concatenated to a weighted fully connected layer. The initial methods and results of the binaural RNN speech enhancement algorithm are published in [17], in which we increase the amount of training data for the neural network and update the loss function to obtain better results. In addition, the report was the first to implement the algorithm in smartphones and to discuss the consequent power consumption and latency.…”

Section: Advanced Speech Enhancement Algorithmmentioning

confidence: 99%

“…However, many studies have proved that based speech enhancement algorithms based on machine learning can improve speech intelligibility in complex environments [14], [15]. Currently, binaural HAs (BHAs) are used by people with HL to provide a more natural experience [16], [17]. With BHAs, an HA device goes in each ear, and the HAs can transmit voice signals from one side to the other side [18].…”

Section: Introductionmentioning

confidence: 99%

A Smart Binaural Hearing Aid Architecture Leveraging a Smartphone APP With Deep-Learning Speech Enhancement

Chen

Sun

et al. 2020

IEEE Access

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This paper presents a smartphone-based binaural hearing aid architecture for improving the speech intelligibility of hearing aid users. The proposed system consists of an earpiece, a smartphone and an application that performs real-time speech enhancement. The speaker's voice, which is picked up by the microphone of the earpiece that is worn on the ear, is transmitted to the smartphone via wireless technology. After the speech intelligibility is improved in real time by the deep learning speech enhancement application, it is returned to the earpiece and generates sound. Deep learning speech enhancement algorithms can be used without performing burdensome calculations on the processors in the hearing aid. The results showed that the average usage of the central processing unit in the smartphone was approximately 26%, and the signal-tonoise ratios improve by at least 20%. The presented objective and subjective results show that the proposed method achieves comparatively more noise suppression without distorting the audio. INDEX TERMS Binaural hearing aids, smartphone, real-time se app, e-health, deep learning.

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A New Lightweight Dual-Microphone RNN-VAD Assist Adaptive Blocking Matrix for Dual-Channel Speech Enhancement

Sun

Zhou

et al. 2021

2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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An RNN-based Speech Enhancement Method for a Binaural Hearing Aid System

Cited by 4 publications

References 11 publications

Speckle Noise Detection and Removal for Laser Speech Measurement Systems

Speckle Noise Detection and Removal for Laser Speech Measurement Systems

A Smart Binaural Hearing Aid Architecture Leveraging a Smartphone APP With Deep-Learning Speech Enhancement

A New Lightweight Dual-Microphone RNN-VAD Assist Adaptive Blocking Matrix for Dual-Channel Speech Enhancement

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