11 research outputs found

    Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates

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    This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when very short utterances are processed, e.g., in voice assistant scenarios. We consider several variants of a system that performs beamforming supported by DNN-based voice activity detection (VAD) followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently in order to make the method applicable in highly dynamic environments. Owing to the short length of the processed block, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to the processing regime when recordings are treated as one block (batch processing). The experimental evaluation of the proposed method is performed on large datasets of CHiME-4 and on another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria (such as signal-to-interference ratio (SIR) or perceptual evaluation of speech quality (PESQ), respectively). Moreover, word error rate (WER) achieved by a baseline automatic speech recognition system is evaluated, for which the enhancement method serves as a front-end solution. The results indicate that the proposed method is robust with respect to short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article accepted for publication in IET Signal Processing journal. Original results unchanged, additional experiments presented, refined discussion and conclusion

    Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence

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    Performance Analysis of Source Image Estimators in Blind Source Separation

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    LNCS 9237 - Proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation

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    International audienceThis book constitutes the proceedings of the 12th International Conference on LatentVariable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic,in August 2015. The 61 revised full papers presented – 29 accepted as oral presentationsand 32 accepted as poster presentations – were carefully reviewed and selected fromnumerous submissions. Five special topics are addressed: tensor-based methods for blindsignal separation; deep neural networks for supervised speech separation/enhancement;joined analysis of multiple datasets, data fusion, and related topics; advances in nonlinearblind source separation; sparse and low rank modeling for acoustic signal processin

    LNCS 9237 - Proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation

    No full text
    International audienceThis book constitutes the proceedings of the 12th International Conference on LatentVariable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic,in August 2015. The 61 revised full papers presented – 29 accepted as oral presentationsand 32 accepted as poster presentations – were carefully reviewed and selected fromnumerous submissions. Five special topics are addressed: tensor-based methods for blindsignal separation; deep neural networks for supervised speech separation/enhancement;joined analysis of multiple datasets, data fusion, and related topics; advances in nonlinearblind source separation; sparse and low rank modeling for acoustic signal processin

    A Treatment of EEG Data by Underdetermined Blind Source Separation for Motor Imagery Classification

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    Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201
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