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In this paper, we propose a novel real-time monaural speech enhancement algorithm by combining the convolutional recurrent network (CRN) and Wiener filter.
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In this study, we have proposed a novel dilated convolutional recurrent neural network for real-time monaural speech enhancement.
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Monaural Speech sentence examples within monaural speech enhancement
Deep Neural Network (DNN)-based mask estimation approach is an emerging algorithm in monaural speech enhancement.
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However, most monaural speech enhancement (SE) models introduce processing artifacts and thus degrade the performance of downstream tasks, including automatic speech recognition (ASR).
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Monaural Speech sentence examples within monaural speech separation
In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss.
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In this article, we propose monaural speech separation that utilizes the speaker embedding in the later separator blocks, which is extracted from the intermediate separated results obtained by the early stages of the separator network.
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Monaural Speech sentence examples within monaural speech recognition
However, this approach could reduce monaural speech recognition and head-shadow benefit by excluding low- or high-frequency information from one ear.
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Design: For 160 participants with normal hearing that were divided into three groups with different simulated hearing thresholds, monaural speech recognition for the Freiburg monosyllabic speech test was obtained via headphones in quiet at different presentation levels.
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Deep Neural Network (DNN)-based mask estimation approach is an emerging algorithm in monaural speech enhancement.
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However, most monaural speech enhancement (SE) models introduce processing artifacts and thus degrade the performance of downstream tasks, including automatic speech recognition (ASR).
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Deep complex U-Net structure and convolutional recurrent network (CRN) structure achieve state-of-the-art performance for monaural speech enhancement.
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In this paper, we propose a novel real-time monaural speech enhancement algorithm by combining the convolutional recurrent network (CRN) and Wiener filter.
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In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss.
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To address this issue, we propose a novel complex spectral mapping approach with a two-stage pipeline for monaural speech enhancement in the time-frequency domain.
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In this paper, we propose a two-stage learning and fusion network with noise awareness for time-domain monaural speech enhancement, which can be regarded as a progressive learning process.
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In this article, we propose monaural speech separation that utilizes the speaker embedding in the later separator blocks, which is extracted from the intermediate separated results obtained by the early stages of the separator network.
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Using monaural speech separation to get human voice and then doing spoofing detection later may be a natural idea, but it has two shortcomings: two models must be trained, and during first separation step, some voice will be filtered out while noise will be kept.
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To solve the above problem, we propose an attention-augmented fully convolutional neural network for monaural speech enhancement.
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Deep neural network (DNN) based end-to-end optimization in the complex time-frequency (T-F) domain or time domain has shown considerable potential in monaural speech separation.
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Particularly, it has been shown that a monaural speech separation task can be successfully solved with a DNN-based method called deep clustering (DC), which uses a DNN to describe the process of assigning a continuous vector to each time-frequency (TF) bin and measure how likely each pair of TF bins is to be dominated by the same speaker.
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In this work, a subjective evaluation of the Electrocodec is presented, which investigates the impact of the codec on monaural speech performance.
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The goal of monaural speech enhancement is to separate clean speech from noisy speech.
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In this chapter, we propose a recursive noise estimation-based Wiener filtering (WF-RANS) approach for monaural speech enhancement.
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Convolutional neural network (CNN) based methods, such as the convolutional encoder-decoder network, offer state-of-the-art results in monaural speech enhancement.
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Monaural speech separation is a very challenging task, for the limitation of information from only a single channel.
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However, this approach could reduce monaural speech recognition and head-shadow benefit by excluding low- or high-frequency information from one ear.
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The monaural speech separation technology is far from satisfactory and has been a challenging task due to the interference of multiple sound sources.
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The performance of the deep neural networks (DNNs) based monaural speech enhancement methods is still limited in real room environments, particularly for the speaker-independent case.
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While some bilateral-CI listeners show a similar binaural advantage, bilateral-CI listeners with relatively large asymmetries in monaural speech understanding can instead experience contralateral speech interference.
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We tested 39 APD children and 25 control children aged between 6 and 12 years via (a) clinical APD tests, including a monaural speech in noise test, (b) isochrony task, a test measuring the detection of small deviations from perfect isochrony in a isochronous beats sequence, and (c) two cognitive tests (auditory memory and auditory attention).
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Design: For 160 participants with normal hearing that were divided into three groups with different simulated hearing thresholds, monaural speech recognition for the Freiburg monosyllabic speech test was obtained via headphones in quiet at different presentation levels.
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Over the past few decades, monaural speech separation has always been an interesting but challenging problem.
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To train a DNN for mask-based beamforming, loss functions designed for monaural speech enhancement/separation have been employed.
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In this study, we have proposed a novel dilated convolutional recurrent neural network for real-time monaural speech enhancement.
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The proposed model incorporates dilated convolutions for tracking a target speaker through context aggregations, skip connections, and residual learning for mapping-based monaural speech enhancement.
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Owing to a paucity of information on the application of this method, especially in a speech system, evaluation of some cost functions in NMF-based monaural speech decomposition was investigated in this study.
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This paper proposes a novel audio-visual-speaker speech separation model that decomposes a monaural speech signal into two speech segments belonging to different speakers, by making use of audio-visual inputs and i-vector speaker embeddings.
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We describe a monaural speech enhancement algorithm based on modulation-domain Kalman filtering to blindly track the time–frequency log-magnitude spectra of speech and reverberation.
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Monaural speech separation techniques are far from satisfactory and are a challenging task due to interference from multiple sources.
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Monaural speech enhancement is a challenging problem because the desired signal is estimated from singlechannel recordings.
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This paper proposes an autoregressive approach to harness the power of deep learning for multi-speaker monaural speech separation.
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In this paper, we propose speech/music pitch classification based on recurrent neural network (RNN) for monaural speech segregation from music interferences.
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In this paper we present an integrated simple and effective end-to-end approach called FurcaX1 to monaural speech separation, which consists of deep gated (de)convolutional neural networks (GCNN) that takes the mixed utterance of two speakers and maps it to two separated utterances, where each utterance contains only one speaker’s voice.
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According to the training objectives, DNN-based monaural speech separation is categorized into three aspects, namely masking, mapping, and signal approximation based techniques.
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Monaural speech enhancement has made dramatic advances since the introduction of deep learning a few years ago.
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To leverage long-term contexts for tracking a target speaker, we treat speech enhancement as a sequence-to-sequence mapping, and present a novel convolutional neural network (CNN) architecture for monaural speech enhancement.
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The performance of the audio-only neural networks based monaural speech separation methods is still limited, particularly when multiple-speakers are active.
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