Multi-Task Learning U-Net for Single-Channel Speech Enhancement and Mask-Based Voice Activity Detection
نویسندگان
چکیده
منابع مشابه
Multi-objective learning and mask-based post-processing for deep neural network based speech enhancement
We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-power spectra (LPS) features to be used directly for constructing the enhanced speech signals. In deep neural network (DNN) based SE we introduce an auxiliary structure to learn secondary continuous features, such as ...
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Distortion of the underlying speech is a common problem for single-channel speech enhancement algorithms, and hinders such methods from being used more extensively. A dictionary based speech enhancement method that emphasizes preserving the underlying speech is proposed. Spectral patches of clean speech are sampled and clustered to train a dictionary. Given a noisy speech spectral patch, the be...
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Spectral mask estimation using bidirectional long short-term memory (BLSTM) neural networks has been widely used in various speech enhancement applications, and it has achieved great success when it is applied to multichannel enhancement techniques with a mask-based beamformer. However, when these masks are used for single channel speech enhancement they severely distort the speech signal and m...
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Unlike single microphone techniques for voice activity detection (VAD), multi-microphone signal processing usually exploits the spatial information of signals received at multiple microphones. In this paper, we propose a VAD algorithm based on conic constraints to achieve robustness against the direction of arrival (DOA) estimation error. The proposed algorithm uses the phase vector as feature ...
متن کاملSupervised single-channel speech enhancement using ratio mask with joint dictionary learning
More comparisons and analyses about the paper "Supervised Single-Channel Speech Enhancement Using Ratio Mask with Joint Dictionary Learning" mentioned are given in this document. Specifically, 1) comparisons between the clean spectra/IRM and reconstructed spectra/IRM in the training stage are done; 2) the influence of the speech dominant parameter performed on the SNMF algorithms are shown; 3...
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ژورنال
عنوان ژورنال: Applied Sciences
سال: 2020
ISSN: 2076-3417
DOI: 10.3390/app10093230