Sound Localization and Speech Enhancement Algorithm Based on Dual-Microphone

Tao, Tao; Hong, Zheng; Yang, Jianfeng; Guo, Zhongyuan; Zhang, Yiyang; Ao, Jiahui; Chen, Yuao; Lin, Wei-Cheng; Tan, Xiao

doi:10.3390/s22030715

Cited by 15 publications

(8 citation statements)

References 26 publications

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“…Tao et al [13] presented the enhanced sound source localization and speech enhancement algorithm which reduce microphone cost and also reduces the complexity. The dual-microphone sound algorithm effectively identifies the sound location, as well as the speech enhancement algorithm, is more resilient and adaptive than the previous method, according to experimental data.…”

Section: Literature Surveymentioning

confidence: 99%

“…enhanced sound source localization and speech enhancement algorithm [13] reduce microphone cost and also reduces the complexity Based on the literature review, a variety of deep learning techniques for improving speech were suggested. However, speech enhancement systems face challenges from unstable voice signals, poor microphone performance, expensive computing, and the problem of echo cancellation in the presence of background noise.…”

Section: Do Not Eliminate Echomentioning

confidence: 99%

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Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

Pathrose,

Ismail M,

Mohan P

2023

Int. j. electr. comput. eng. syst. (Online)

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In general, in-car speech enhancement is an application of the microphone array speech enhancement in particular acoustic environments. Speech enhancement inside the moving cars is always an interesting topic and the researchers work to create some modules to increase the quality of speech and intelligibility of speech in cars. The passenger dialogue inside the car, the sound of other equipment, and a wide range of interference effects are major challenges in the task of speech separation in-car environment. To overcome this issue, a novel Beamforming based Deep learning Network (Bf-DLN) has been proposed for speech enhancement. Initially, the captured microphone array signals are pre-processed using an Adaptive beamforming technique named Least Constrained Minimum Variance (LCMV). Consequently, the proposed method uses a time-frequency representation to transform the pre-processed data into an image. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used for converting time-domain speech inputs into images. Convolutional deep belief network (CDBN) is used to extract the most pertinent features from these transformed images. Enhanced Elephant Heard Algorithm (EEHA) is used for selecting the desired source by eliminating the interference source. The experimental result demonstrates the effectiveness of the proposed strategy in removing background noise from the original speech signal. The proposed strategy outperforms existing methods in terms of PESQ, STOI, SSNRI, and SNR. The PESQ of the proposed Bf-DLN has a maximum PESQ of 1.98, whereas existing models like Two-stage Bi-LSTM has 1.82, DNN-C has 1.75 and GCN has 1.68 respectively. The PESQ of the proposed method is 1.75%, 3.15%, and 4.22% better than the existing GCN, DNN-C, and Bi-LSTM techniques. The efficacy of the proposed method is then validated by experiments.

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Section: Literature Surveymentioning

confidence: 99%

Section: Do Not Eliminate Echomentioning

confidence: 99%

Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

Pathrose,

Ismail M,

Mohan P

2023

Int. j. electr. comput. eng. syst. (Online)

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“…The results showed that the suggested ImNMF can significantly improve no ise speech while also increasing the robustness of the signal in electric car noise environment. Tao et al [13] presented the enhanced speech signal source localization and enhancement system which reduces microphone cost and reduces complexity. Dual-microphone sound algorithm effectively identifies the sound location, as well as the speech quality improvement, is more resilient and adaptive than the previous method, according to experimental data.…”

Section: Literature Surveymentioning

confidence: 99%

“…where C is the speed of sound in air. As a result, (Ɵ) can be used to estimate the direction as (13),…”

Section: The Direction Of Arrival (Doa)mentioning

confidence: 99%

In-car speech enhancement based on source separation technique

Pathrose,

Ismail

2023

IJRA

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This work addresses the solution of localizing and enhancing hands-free speech inside the car environment. Cars have different types of sounds from outside, co-passengers dialogue and noise. To provide better-quality speech, a microphone array-based beamforming technique is used. This research work proposes the method for selected source localization, source separation, and enhancement. An estimation of the direction of arrival (DOA) to localize the signal direction and preferred direction is selected for speech enhancement. The spiral and sine-cosine algorithm (SSCA) algorithm is combined with an adaptive least mean square to adapt the system for different environments. The algorithm is implemented in hardware and tested in a real-time car environment. The results showed significant improvement in signal-to-noise ratio (SNR) of 5.2 dB and perceptual evaluation of speech quality (PESQ) of 2.3. Finally, the model is fine-tuned for the car to get better quality. The proposed technique is efficient, and results are compared with existing methods.

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“…Over the past decades, there has been a growing demand for speech enhancement using microphone arrays in speech processing applications such as automatic speech recognition, mobile communications, and hearing aids [ 1 , 2 , 3 , 4 ]. Multichannel speech enhancement aims to reduce the additive noise and improve the quality of the speech signals obtained by multiple microphones placed in a variety of acoustic environments [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. In many multichannel speech enhancement systems, beamforming algorithms, such as the minimum-variance distortionless-response (MVDR) beamformer [ 11 ] and the general transfer function generalized sidelobe canceler (TF-GSC) [ 12 , 13 ], have been employed to extract a desired signal, exploiting spatial information on the location of the sound sources.…”

Section: Introductionmentioning

confidence: 99%

Postfilter for Dual Channel Speech Enhancement Using Coherence and Statistical Model-Based Noise Estimation

Cheong,

Kim,

Shin

2024

Sensors

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A multichannel speech enhancement system usually consists of spatial filters such as adaptive beamformers followed by postfilters, which suppress remaining noise. Accurate estimation of the power spectral density (PSD) of the residual noise is crucial for successful noise reduction in the postfilters. In this paper, we propose a postfilter utilizing proposed a posteriori speech presence probability (SPP) and noise PSD estimators, which are based on both the coherence and the statistical models. We model the coherence-based a posteriori SPP as a simple function of the magnitude of coherence between two microphone signals and combine it with a single-channel SPP based on statistical models. The coherence-based estimator for the PSD of the noise remaining in the beamformer output in the presence of speech is derived using the pseudo-coherence considering the effect of the beamformers, which is used to construct the coherence-based noise PSD estimator. Then, the final noise PSD estimator is obtained by combining the coherence-based and statistical model-based noise PSD estimators with the proposed SPP. The spectral gain function is also modified, incorporating the proposed SPP. Experimental results demonstrate that the proposed method led to more accurate noise PSD estimation and perceptual evaluation of speech quality scores in various diffuse noise environments, and did not degrade the speech quality under the presence of directional interference, although the proposed method utilizes the coherence information.

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Sound Localization and Speech Enhancement Algorithm Based on Dual-Microphone

Cited by 15 publications

References 26 publications

Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

In-car speech enhancement based on source separation technique

Postfilter for Dual Channel Speech Enhancement Using Coherence and Statistical Model-Based Noise Estimation

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