Real-Time Speech Enhancement Using Equilibriated RNN

Takeuchi, Daisuke; Yatabe, Kohei; Oikawa, Yasuhiro; Harada, Noboru

doi:10.1109/icassp40776.2020.9054597

Cited by 38 publications

(25 citation statements)

References 25 publications

(51 reference statements)

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“…In every recurring round, RNN is compatible with sequential data and time-series as the network structure is particularly developed for representing historical information [ 164 ]. Problems based on sequence mapping are widely solved by using RNN such as reinforcement learning, handwriting recognition, speech recognition, and sequence generation because of the characteristic of propagating previous information along with time via recurring connections [ 165 , 166 , 167 , 168 ]. For the detection of interactions between proteins and genes, biomedical researchers are now using RNN [ 169 ].…”

Section: Machine Learning For Biosensorsmentioning

confidence: 99%

Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope

et al. 2021

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The electrochemical biosensors are a class of biosensors which convert biological information such as analyte concentration that is a biological recognition element (biochemical receptor) into current or voltage. Electrochemical biosensors depict propitious diagnostic technology which can detect biomarkers in body fluids such as sweat, blood, feces, or urine. Combinations of suitable immobilization techniques with effective transducers give rise to an efficient biosensor. They have been employed in the food industry, medical sciences, defense, studying plant biology, etc. While sensing complex structures and entities, a large data is obtained, and it becomes difficult to manually interpret all the data. Machine learning helps in interpreting large sensing data. In the case of biosensors, the presence of impurity affects the performance of the sensor and machine learning helps in removing signals obtained from the contaminants to obtain a high sensitivity. In this review, we discuss different types of biosensors along with their applications and the benefits of machine learning. This is followed by a discussion on the challenges, missing gaps in the knowledge, and solutions in the field of electrochemical biosensors. This review aims to serve as a valuable resource for scientists and engineers entering the interdisciplinary field of electrochemical biosensors. Furthermore, this review provides insight into the type of electrochemical biosensors, their applications, the importance of machine learning (ML) in biosensing, and challenges and future outlook.

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Section: Machine Learning For Biosensorsmentioning

confidence: 99%

Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope

et al. 2021

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“…Many researchers proposed the recurrent neural network as a reliable model for time series problems. However, one of the limitations of RNN suffers from vanishing and exploding gradients, which lead to problems during the training phase (Takeuchi et al, 2020). To overcome this problem, long short-term memory was developed.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Afan

Osman

Essam

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia.

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“…There has also been an increase in the use of modified versions of conventional neural networks. For example, Takeuchi et al [22] proposed a real-time SE system using equilibrated recurrent neural network to solve the problem of vanishing or exploding gradient without increasing the number of parameters within the network.…”

Section: Introductionmentioning

confidence: 99%

Towards More Efficient DNN-Based Speech Enhancement Using Quantized Correlation Mask

2021

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Many studies on deep learning-based speech enhancement (SE) utilizing the computational auditory scene analysis method typically employs the ideal binary mask or the ideal ratio mask to reconstruct the enhanced speech signal. However, many SE applications in real scenarios demand a desirable balance between denoising capability and computational cost. In this study, first, an improvement over the ideal ratio mask to attain more superior SE performance is proposed through introducing an efficient adaptive correlation-based factor for adjusting the ratio mask. The proposed method exploits the correlation coefficients among the noisy speech, noise and clean speech to effectively re-distribute the power ratio of the speech and noise during the ratio mask construction phase. Second, to make the supervised SE system more computationally-efficient, quantization techniques are considered to reduce the number of bits needed to represent floating numbers, leading to a more compact SE model. The proposed quantized correlation mask is utilized in conjunction with a 4-layer deep neural network (DNN-QCM) comprising dropout regulation, pre-training and noise-aware training to derive a robust and high-order mapping in enhancement, and to improve generalization capability in unseen conditions. Results show that the quantized correlation mask outperforms the conventional ratio mask representation and the other SE algorithms used for comparison. When compared to a DNN with ideal ratio mask as its learning targets, the DNN-QCM provided an improvement of approximately 6.5% in the short-time objective intelligibility score and 11.0% in the perceptual evaluation of speech quality score. The introduction of the quantization method can reduce the neural network weights to a 5-bit representation from a 32-bit, while effectively suppressing stationary and non-stationary noise. Timing analyses also show that with the techniques incorporated in the proposed DNN-QCM system to increase its compactness, the training and inference time can be reduced by 15.7% and 10.5%, respectively. INDEX TERMSCorrelation coefficients, deep neural network, dynamic noise-aware training, quantization, speech enhancement, training targets.

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Real-Time Speech Enhancement Using Equilibriated RNN

Cited by 38 publications

References 25 publications

Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope

Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Towards More Efficient DNN-Based Speech Enhancement Using Quantized Correlation Mask

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