A wearable bone-conducted speech enhancement system for strong background noises

Huang, Boyan; Gong, Yihan; Sun, Jinwei; Shen, Yuelei

doi:10.1109/icept.2017.8046759

Cited by 15 publications

(4 citation statements)

References 3 publications

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“…To overcome this a wearable bone conducted speech system was implemented and signals were collected from various skull positions which have very high frequency components and compared with air conducted speech signals. According to Boyan Haung et al [22], in order to enhance the Bone and Air Conducted speech signal, employing FIR filter alone may not shows better performance, using trigonometric expansions of FLANN filter combined together be the better signal enhance. The proposed scheme have smaller MSE with recovered speech signal and clean speech signals this shown in Table 2.2.…”

Section: Brief Review On Approachesmentioning

confidence: 99%

A Study to Analyze Enhancement Techniques on Sound Quality for Bone Conduction and Air Conduction Speech Processing

Subbaiah

Deepthi

2020

SCPE

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In recent years, enhancing speech signal for communications become more complex due to the environmental noises for military and navel applications. Many researchers analyzed and developed complex algorithms and expressed ways to enhance speech signal for both Bone Conducted Speech (BCS) and Air Conducted Speech (ACS) processing techniques. BCS signal is vigorous for outer environmental noises and with low quality signal. ACS signal is sensitive for environmental noises nut rigid in amplitude and high frequencies.Here we have reviewed and investigated various articles to explore their ideas in speech processing for enhancing intelligibility of sound quality.

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Section: Brief Review On Approachesmentioning

confidence: 99%

A Study to Analyze Enhancement Techniques on Sound Quality for Bone Conduction and Air Conduction Speech Processing

Subbaiah

Deepthi

2020

SCPE

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“…The first one is to explore the non-linear mapping of BC speech signals to AC speech signals [ 14 , 15 , 16 , 17 ]. Recently, a deep neural network was used to map the spectral coefficients of the linear prediction coding of BC speech to the coefficients of AC speech [ 18 ]. Liu et al utilized a deep noise reduction autoencoder to achieve the abovementioned mapping [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Robust Dual-Microphone Generalized Sidelobe Canceller Using a Bone-Conduction Sensor for Speech Enhancement

Zhou

Wang

Chu

et al. 2021

Sensors

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The use of multiple spatially distributed microphones allows performing spatial filtering along with conventional temporal filtering, which can better reject the interference signals, leading to an overall improvement of the speech quality. In this paper, we propose a novel dual-microphone generalized sidelobe canceller (GSC) algorithm assisted by a bone-conduction (BC) sensor for speech enhancement, which is named BC-assisted GSC (BCA-GSC) algorithm. The BC sensor is relatively insensitive to the ambient noise compared to the conventional air-conduction (AC) microphone. Hence, BC speech can be analyzed to generate very accurate voice activity detection (VAD), even in a high noise environment. The proposed algorithm incorporates the VAD information obtained by the BC speech into the adaptive blocking matrix (ABM) and adaptive noise canceller (ANC) in GSC. By using VAD to control ABM and combining VAD with signal-to-interference ratio (SIR) to control ANC, the proposed method could suppress interferences and improve the overall performance of GSC significantly. It is verified by experiments that the proposed GSC system not only improves speech quality remarkably but also boosts speech intelligibility.

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“…In early algorithms [10]- [13], transmission channel functions were represented by low dimensional spectral envelope features, Gaussian Mixture Models (GMM) and shallow neural networks were often employed to learn the mapping relationship. Recently, deep neural networks (DNN) have been used to learn the complex nonlinear mapping relationship [14], [15], and some researchers start to use high-dimensional features to represent the difference of the two speech. For instance, deep denoising autoencoder is used to map the high dimensional Mel magnitude spectra of the two speech and achieves considerable improvements [16].…”

Section: Introductionmentioning

confidence: 99%

Spectra Restoration of Bone-Conducted Speech via Attention-Based Contextual Information and Spectro-Temporal Structure Constraint

Zheng

Cao

Yang

et al. 2019

IEICE Trans. Fundamentals

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Compared with acoustic microphone (AM) speech, boneconducted microphone (BCM) speech is much immune to background noise, but suffers from severe loss of information due to the characteristics of the human-body transmission channel. In this letter, a new method for the speaker-dependent BCM speech enhancement is proposed, in which we focus our attention on the spectra restoration of the distorted speech. In order to better infer the missing components, an attention-based bidirectional Long Short-Term Memory (AB-BLSTM) is designed to optimize the use of contextual information to model the relationship between the spectra of BCM speech and its corresponding clean AM speech. Meanwhile, a structural error metric, Structural SIMilarity (SSIM) metric, originated from image processing is proposed to be the loss function, which provides the constraint of the spectro-temporal structures in recovering of the spectra. Experiments demonstrate that compared with approaches based on conventional DNN and mean square error (MSE), the proposed method can better recover the missing phonemes and obtain spectra with spectro-temporal structure more similar to the target one, which leads to great improvement on objective metrics.

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A wearable bone-conducted speech enhancement system for strong background noises

Cited by 15 publications

References 3 publications

A Study to Analyze Enhancement Techniques on Sound Quality for Bone Conduction and Air Conduction Speech Processing

A Study to Analyze Enhancement Techniques on Sound Quality for Bone Conduction and Air Conduction Speech Processing

A Robust Dual-Microphone Generalized Sidelobe Canceller Using a Bone-Conduction Sensor for Speech Enhancement

Spectra Restoration of Bone-Conducted Speech via Attention-Based Contextual Information and Spectro-Temporal Structure Constraint

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