An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and Acoustic Modeling for Robust Speech Recognition

Wu, Bo; Li, Kehuang; Ge, Fengpei; Huang, Zhen; Yang, Minglei; Siniscalchi, Sabato Marco; Lee, Chin‐Hui

doi:10.1109/jstsp.2017.2756439

Cited by 69 publications

(33 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DL is the foundation of many modern Artificial Intelligence (AI) applications, which consists of multiple hidden layers and neural nodes. At present, it has been wildly utilized in image recognition [20]- [22], voice processing [23], [24], etc., and some progress in communication has been achieved [25]- [27]. Modulation recognition of digital signals has gradually changed from the traditional method to DNN method with the rapid development of DL.…”

Section: B Deep Neural Network Modelmentioning

confidence: 99%

Particle Swarm Optimization-Based Deep Neural Network for Digital Modulation Recognition

et al. 2019

View full text Add to dashboard Cite

Modulation recognition is a major task in many wireless communication systems including cognitive radio and signal reconnaissance. The diversification of modulation schemes and the increased complexity of the channel environment put higher requirements on the correct identification of modulated signals. Deep learning (DL) is considered as a potential solution to solve these problems due to the superior big data processing and classification capabilities. This paper proposes an efficient digital modulation recognition method based on deep neural network (DNN) model. Furthermore, we present the particle swarm optimization (PSO) algorithm to optimize the number of hidden layer nodes of the DNN so as to solve the problem that the traditional DNN is trapped in local minimum values and the number of hidden layer nodes needs selecting manually. In this paper, we utilize the proposed PSO-DNN method to learn characteristics extracted from the modulated signal added by additive white Gaussian noise (AWGN) and to train the network, which can improve the performance of recognition under the condition of low signalto-noise ratio (SNR). The experimental results demonstrate that the recognition rate on this algorithm has improved by 9.4% and 8.8% compared with methods that adopt conventional DNN and support vector machine (SVM) when SNR equals 0 and 1 dB, respectively. Besides, another experiment compared with the genetic algorithm (GA) also proves that our proposed algorithm is more effective in optimizing the DNN. The proposed method is easy to be implemented so that it has a broad development prospect in modulation recognition.INDEX TERMS Additive white Gaussian noise, deep neural network, digital modulation recognition, particle swarm optimization algorithm.

show abstract

Section: B Deep Neural Network Modelmentioning

confidence: 99%

Particle Swarm Optimization-Based Deep Neural Network for Digital Modulation Recognition

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Therefore, the current *Corresponding Author Institutional Email: motameni@iausari.ac.ir (H. Motameni) study has covered nonstandard sentences of Persian language. One of the most important reasons behind doing such research is that the results are exclusively applicable to a number of linguistic areas such as smart filtering [2], machine translation [3], speech recognition [4,5], text recognition [6], text summarization [7], etc. The majority of Persian linguistic research projects adopt traditional methods of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Determining the Composition Functions of Persian Non-standard Sentences in Terminology using a Deep Learning Fuzzy Neural Network Model

Motameni

2020

IJE

View full text Add to dashboard Cite

“…Recently, as an important branch of the field of artificial intelligence, the deep learning technology, has been applied to all popular artificial intelligence areas, including speech recognition, image recognition, big data analysis, etc. [5][6][7]. Especially, the convolutional neural network (CNN), which is well-known for its strong ability to extract features, has gained enormous attention in the field of image classification and image recognition.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

2019

View full text Add to dashboard Cite

Combined cooling, heating, and power (CCHP) systems is a distributed energy system that uses the power station or heat engine to generate electricity and useful heat simultaneously. Due to its wide range of advantages including efficiency, ecological, and financial, the CCHP will be the main direction of the integrated system. The accurate prediction of heating, gas, and electrical loads plays an essential role in energy management in CCHP systems. This paper combined long short-term memory (LSTM) network and convolutional neural network (CNN) to design a novel hybrid neural network for short-term loads forecasting considering their correlation. Pearson correlation coefficient will be utilized to measure the temporal correlation between current load and historical loads, and analyze the coupling between heating, gas and electrical loads. The dropout technique is proposed to solve the over-fitting of the network due to the lack of data diversity and network parameter redundancy. The case study shows that considering the coupling between heating, gas and electrical loads can effectively improve the forecasting accuracy, the performance of the proposed approach is better than that of the traditional methods.

show abstract

An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and Acoustic Modeling for Robust Speech Recognition

Cited by 69 publications

References 55 publications

Particle Swarm Optimization-Based Deep Neural Network for Digital Modulation Recognition

Particle Swarm Optimization-Based Deep Neural Network for Digital Modulation Recognition

Determining the Composition Functions of Persian Non-standard Sentences in Terminology using a Deep Learning Fuzzy Neural Network Model

Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

Contact Info

Product

Resources

About