Deep ad-hoc beamforming based on speaker extraction for target-dependent speech separation

Yang, Ziye; Guan, Shanzheng; Zhang, Xiaolei

doi:10.1016/j.specom.2022.04.002

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…However, the channel selection approach is not optimized. Recently, many works explored advanced channel selection approaches for speech enhancement [24], [25], speech separation [26]- [28], speech recognition [29], and speaker recognition [30], [31]. Particularly, Liang et al [30] and Cai et al [31] independently proposed end-to-end speaker verification with ad-hoc microphone arrays, where an inter-channel attention-based channel reweighting method was developed to fuse utterance-level speaker features from all channels.…”

Section: Arxiv:230701386v1 [Cssd] 3 Jul 2023mentioning

confidence: 99%

Scaling Sparsemax Based Channel Selection for Speech Recognition with ad-hoc Microphone Arrays

Chen¹,

Zhang²

2021

Interspeech 2021

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Recently, speech recognition with ad-hoc microphone arrays has received much attention. It is known that channel selection is an important problem of ad-hoc microphone arrays, however, this topic seems far from explored in speech recognition yet, particularly with a large-scale ad-hoc microphone array. To address this problem, we propose a Scaling Sparsemax algorithm for the channel selection problem of the speech recognition with large-scale ad-hoc microphone arrays. Specifically, we first replace the conventional Softmax operator in the stream attention mechanism of a multichannel end-to-end speech recognition system with Sparsemax, which conducts channel selection by forcing the channel weights of noisy channels to zero. Because Sparsemax punishes the weights of many channels to zero harshly, we propose Scaling Sparsemax which punishes the channels mildly by setting the weights of very noisy channels to zero only. Experimental results with ad-hoc microphone arrays of over 30 channels under the conformer speech recognition architecture show that the proposed Scaling Sparsemax yields a word error rate of over 30% lower than Softmax on simulation data sets, and over 20% lower on semi-real data sets, in test scenarios with both matched and mismatched channel numbers.

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Section: Arxiv:230701386v1 [Cssd] 3 Jul 2023mentioning

confidence: 99%

Scaling Sparsemax Based Channel Selection for Speech Recognition with ad-hoc Microphone Arrays

Chen¹,

Zhang²

2021

Interspeech 2021

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End-to-end Two-dimensional Sound Source Localization With Ad-hoc Microphone Arrays

Gong

Liu

Zhang

2022

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

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Recently, an end-to-end two-dimensional sound source localization algorithm with ad-hoc microphone arrays formulates the sound source localization problem as a classification problem. The algorithm divides the target indoor space into a set of local areas, and predicts the local area where the speaker locates. However, the local areas are encoded by one-hot code, which may lose the connections between the local areas due to quantization errors. In this paper, we propose a new soft label coding method, named label smoothing, for the classificationbased two-dimensional sound source location with ad-hoc microphone arrays. The core idea is to take the geometric connection between the classes into the label coding process.The first one is named static soft label coding (SSLC), which modifies the one-hot codes into soft codes based on the distances between the local areas. Because SSLC is handcrafted which may not be optimal, the second one, named dynamic soft label coding (DSLC), further rectifies SSLC, by learning the soft codes according to the statistics of the predictions produced by the classification-based localization model in the training stage. Experimental results show that the proposed methods can effectively improve the localization accuracy.

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MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array

Zhang

et al. 2023

Sensors

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Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance. The experimental results showed that this method could achieve high-accuracy fault distance identification in a noisy environment and had better accuracy than the conventional beamforming algorithm (CBF)-LSTM and functional beamforming algorithm (FBF)-LSTM. In addition, this method could also be applied to other industrial testing fields and has a wide range of application prospects.

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Deep ad-hoc beamforming based on speaker extraction for target-dependent speech separation

Cited by 3 publications

References 31 publications

Scaling Sparsemax Based Channel Selection for Speech Recognition with ad-hoc Microphone Arrays

Scaling Sparsemax Based Channel Selection for Speech Recognition with ad-hoc Microphone Arrays

End-to-end Two-dimensional Sound Source Localization With Ad-hoc Microphone Arrays

MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array

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