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Mask estimation has shown a IoT of promise in speech enhancement for its simplicity and large speech intelligibility improvement. In this paper, the gammachirp filter banks are applied on the contaminated speech signal to get the auditory time-frequency representation. Robust principal component analysis with non-negative constraint is employed to decompose the auditory time-frequency representation...
Monaural speech enhancement is a key yet challenging problem in speech area, which is always used as a pre-processing step of robust speech processing. Deep learning has proved to be very successful for solving this issue. In this paper, a new approach for enhancing the noisy speech in a single channel recording is presented. We propose a modified ideal ratio mask (IRM) which calculated by normalized...
For true mobility, wearable electronics should be self-powered by the environment. On-body thermoelectric (∼50µW/cm2) is a maturing energy source but delivers a deeply low and inconstant output voltage (0.05 to 0.3V) hindering its utility. With the limited power efficiency of ultra-low-voltage (ULV) boost converters (64% in [1]), there is a rising interest in developing ULV radios that can operate...
Speech enhancement plays an important role in robust speech processing. Deep learning has become a new trend towards solving speech enhancement problems. The input feature is a key aspect of deep learning, which effect the enhancement performance. In this paper, we explore a new feature which extract through the minimum mean square error (MMSE) estimator pretreatment. Incorporating the MMSE pretreatment...
The influence of noise on the performance of a low bit rate parametric speech coder is addressed in this paper. MELP vocoder is used to estimate three parameters: the fundamental frequency, voicing and linear prediction coefficients. Influence of different noises under various acoustic environments on the MELP vocoder's parameters is studied. Pitch accuracy rate, voicing decision error rate and average...
Monaural speech enhancement is a key yet challenging problem for many important real world applications. Recently, deep neural networks(DNNs)-based speech enhancement methods, which extract useful feature from complex feature, have demonstrated remarkable performance improvement. In this paper, we present a novel DNN architecture for monaural speech enhancement. Taking into account the masking properties...
A perceptually motivated speech enhancement approach is proposed in this paper. Different from the conventional sparse and low-rank model based approaches, this new approach takes into account the perceptual differences in different frequency bands of the human auditory system, and separates speech from background noises in the Mel spectral domain. After two propositions for the Mel frequency weighted...
Improving the perceptual quality of speech signals is a key yet challenging problem for many real world applications. Taking into account the good performance of deep learning in signal representation, a novel single-channel speech enhancement technique is presented based on joint Deep Neural Networks and audible noise suppression as a whole network architecture. This new deep neural network jointly...
Improving the perceptual quality of speech signals is a key yet challenging problem for many real world applications. In this paper, we propose a perceptually motivated approach based on deep neural networks (DNNs) for speech enhancement task. The proposed approach take into consider the masking properties of the human auditory system and reduces the perceptual effect of the residual noise. Given...
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