Introduction to Speech Enhancement
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Speech Enhancement sentence examples within deep neural network
Speech Enhancement sentence examples within convolutional neural network
The existing convolutional neural network (CNN) based methods still have limitations in model accuracy, latency and computational cost for single channel speech enhancement.
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In recent years, convolutional neural networks (CNNs) have been widely exploited in deep neural network (DNN)-based speech enhancement methods.
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Speech Enhancement sentence examples within automatic speech recognition
A new cross-entropy-guided measure (CEGM) is proposed to indirectly assess accuracies of automatic speech recognition (ASR) of degraded speech with a speech enhancement front-end and without directly performing ASR experiments.
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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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Speech Enhancement sentence examples within generalized sidelobe canceller
The transfer function-generalized sidelobe canceller (TF-GSC) is one of the most popular structures for the adaptive beamformer used in multi-channel speech enhancement.
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The traditional generalized sidelobe canceller (GSC) is a common speech enhancement front end to improve the noise robustness of automatic speech recognition (ASR) systems in the far-field cases.
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Speech Enhancement sentence examples within linear prediction coefficient
The performance of speech coding, speech recognition, and speech enhancement largely depends upon the accuracy of the linear prediction coefficient (LPC) of clean speech and noise in practice.
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Current augmented Kalman filter (AKF)-based speech enhancement algorithms utilise a temporal convolutional network (TCN) to estimate the clean speech and noise linear prediction coefficient (LPC).
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Speech Enhancement sentence examples within non stationary noise
Deep learning-based speech enhancement algorithms have shown their powerful ability in removing both stationary and non-stationary noise components from noisy speech observations.
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The BAM and three competitive methods, Ideal Binary Mask (IBM), Target Binary Mask (TBM), and Non-stationary Noise Estimation for Speech Enhancement (NNESE), are evaluated considering speech signals corrupted by three non-stationary acoustic noises and six values of signal-to-noise ratio (SNR).
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Speech Enhancement sentence examples within recurrent neural network
Speech Enhancement sentence examples within time frequency domain
Existing speech enhancement methods mainly separate speech from noises at the signal level or in the time-frequency domain.
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Estimating time-frequency domain masks for single-channel speech enhancement using deep learning methods has recently become a popular research field with promising results.
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Speech Enhancement sentence examples within long short term
In this paper, a multi-objective speech enhancement based on the Long-Short Term Memory (LSTM) recurrent neural network (RNN) is proposed to simultaneously estimate the magnitude and phase spectra of clean speech.
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The CRN comprises a convolutional encoder-decoder structure and long short-term memory (LSTM) layers, which have been shown to be suitable for real-time speech enhancement applications.
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Speech Enhancement sentence examples within improve speech quality
Speech Enhancement sentence examples within Channel Speech Enhancement
The transfer function-generalized sidelobe canceller (TF-GSC) is one of the most popular structures for the adaptive beamformer used in multi-channel speech enhancement.
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Moreover, for DNN-based single-channel speech enhancement algorithms, this paper proposes PDNNs and PLSTMs to solve the problem of serious performance degradation of prototype DNN speech enhancement under low signal-to-noise ratio.
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Speech Enhancement 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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Speech Enhancement sentence examples within Multichannel Speech Enhancement
In this paper, we propose \textit{deep ad-hoc beamforming}, a deep-learning-based multichannel speech enhancement framework based on ad-hoc microphone arrays, to address the problem.
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This paper addresses the problem of microphone array generalization for deep-learning-based end-to-end multichannel speech enhancement.
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Speech Enhancement sentence examples within Visual Speech Enhancement
Recently, audio-visual speech enhancement has been tackled in the unsupervised settings based on variational auto-encoders (VAEs), where during training only clean data is used to train a generative model for speech, which at test time is combined with a noise model, e.
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We address unsupervised audio-visual speech enhancement based on variational autoencoders (VAEs), where the prior distribution of clean speech spectrogram is simulated using an encoder-decoder architecture.
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Speech Enhancement sentence examples within Microphone Speech Enhancement
Speech Enhancement sentence examples within Time Speech Enhancement
This paper proposes a full-band and sub-band fusion model, named as FullSubNet, for single-channel real-time speech enhancement.
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The CRN comprises a convolutional encoder-decoder structure and long short-term memory (LSTM) layers, which have been shown to be suitable for real-time speech enhancement applications.
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Speech Enhancement sentence examples within Reverberant Speech Enhancement
Speech Enhancement sentence examples within Binaural Speech Enhancement
Speech Enhancement sentence examples within Robust Speech Enhancement
The experimental results demonstrates that the proposed spectrogram denoising model has better learning ability and denoising performance, whether it is a known noise situation or a noise mismatch situation, so that the proposed system shows robust speech enhancement effect.
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In this paper, a new approach for robust speech enhancement based on improved ensemble empirical mode decomposition (EMD) using optimized log-spectral amplitude noise estimation is presented.
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Speech Enhancement sentence examples within Proposed Speech Enhancement
The resultant is given to wake word engine and alexa (cloud), the proposed speech enhancement algorithm shows significant performance improvements in terms of speech processing evaluation and can also be used to deactivate certain processes during the non-speech signal.
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The resultant is given to wake word engine and alexa (cloud), the proposed speech enhancement algorithm shows significant performance improvements in terms of speech processing evaluation and can also be used to deactivate certain processes during the non-speech signal.
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Speech Enhancement sentence examples within Optimize Speech Enhancement
Previous work has proposed building acoustic echo cancellation (AEC) models for this task that optimize speech enhancement metrics using both neural network as well as signal processing approaches.
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In recent years, research into objective speech intelligibility measures has gained increased interest as a tool to optimize speech enhancement algorithms.
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Speech Enhancement sentence examples within Unsupervised Speech Enhancement
This unsupervised speech enhancement method has an immense ability to decrease the noise in nonstationary and difficult noisy backgrounds.
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By studying the subband mean-squared error (MSE) of the speech for unsupervised speech enhancement approaches and revealing its relationship with the existing loss function for supervised approaches, this paper derives a generalized loss function, when taking the residual noise control into account, for supervised approaches.
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Speech Enhancement sentence examples within Adaptive Speech Enhancement
To solve the problem of directional interference noise in portable voice communication equipment, a dual-channel adaptive speech enhancement algorithm combined with the first-order differential microphone array and variable-step-size frequency-domain Least Mean Square (LMS) algorithm is proposed.
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In this study, we propose a novel noise adaptive speech enhancement (SE) system, which employs a domain adversarial training (DAT) approach to tackle the issue of a noise type mismatch between the training and testing conditions.
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Speech Enhancement sentence examples within Conventional Speech Enhancement
Then, the estimated PRMs at the utterance level are combined within a conventional speech enhancement algorithm at the frame level for speech enhancement.
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Unlike conventional speech enhancement modules that are designed to obtain clean speech signal by removing noise components before speech codec processing, the proposed method directly enhances codec parameters on either the encoder or decoder side.
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Speech Enhancement sentence examples within Improve Speech Enhancement
It also improves speech enhancement performance compared with several state-of-the-art baseline systems.
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In this paper, we propose a novel approach to virtually increasing the number of microphone elements between two real microphones to improve speech enhancement performance in underdetermined situations.
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Speech Enhancement sentence examples within Perform Speech Enhancement
In this study, we propose an attention-based MTL (ATM) approach that integrates MTL and the attention-weighting mechanism to simultaneously realize a multi-model learning structure that performs speech enhancement (SE) and speaker identification (SI).
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Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement.
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Speech Enhancement sentence examples within Common Speech Enhancement
Our preliminary study shows that common speech enhancement methods based on amplitude spectrum estimation can not achieve a satisfactory performance on this task.
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The traditional generalized sidelobe canceller (GSC) is a common speech enhancement front end to improve the noise robustness of automatic speech recognition (ASR) systems in the far-field cases.
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Speech Enhancement sentence examples within Degrade Speech Enhancement
Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance.
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The inaccurate estimates of linear prediction coefficient (LPC) and noise variance 1 introduce bias in Kalman filter (KF) gain and degrades speech enhancement performance.
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Speech Enhancement sentence examples within speech enhancement algorithm
Moreover, for DNN-based single-channel speech enhancement algorithms, this paper proposes PDNNs and PLSTMs to solve the problem of serious performance degradation of prototype DNN speech enhancement under low signal-to-noise ratio.
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It is shown that the parameterized polynomial matrix eigenvalue decomposition (PEVD)-based speech enhancement algorithm exploits the lack of correlation between speech and the late reflections to enhance the speech component associated with the direct path and early reflections.
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Speech Enhancement sentence examples within speech enhancement method
This unsupervised speech enhancement method has an immense ability to decrease the noise in nonstationary and difficult noisy backgrounds.
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In recent years, convolutional neural networks (CNNs) have been widely exploited in deep neural network (DNN)-based speech enhancement methods.
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Speech Enhancement sentence examples within speech enhancement technique