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  • Open Access


    Mobile Communication Voice Enhancement Under Convolutional Neural Networks and the Internet of Things

    Jiajia Yu*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 777-797, 2023, DOI:10.32604/iasc.2023.037354

    Abstract This study aims to reduce the interference of ambient noise in mobile communication, improve the accuracy and authenticity of information transmitted by sound, and guarantee the accuracy of voice information delivered by mobile communication. First, the principles and techniques of speech enhancement are analyzed, and a fast lateral recursive least square method (FLRLS method) is adopted to process sound data. Then, the convolutional neural networks (CNNs)-based noise recognition CNN (NR-CNN) algorithm and speech enhancement model are proposed. Finally, related experiments are designed to verify the performance of the proposed algorithm and model. The experimental results… More >

  • Open Access


    Improving Speech Enhancement Framework via Deep Learning

    Sung-Jung Hsiao1, Wen-Tsai Sung2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3817-3832, 2023, DOI:10.32604/cmc.2023.037380

    Abstract Speech plays an extremely important role in social activities. Many individuals suffer from a “speech barrier,” which limits their communication with others. In this study, an improved speech recognition method is proposed that addresses the needs of speech-impaired and deaf individuals. A basic improved connectionist temporal classification convolutional neural network (CTC-CNN) architecture acoustic model was constructed by combining a speech database with a deep neural network. Acoustic sensors were used to convert the collected voice signals into text or corresponding voice signals to improve communication. The method can be extended to modern artificial intelligence techniques, More >

  • Open Access


    Adversarial Examples Protect Your Privacy on Speech Enhancement System

    Mingyu Dong, Diqun Yan*, Rangding Wang

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1-12, 2023, DOI:10.32604/csse.2023.034568

    Abstract Speech is easily leaked imperceptibly. When people use their phones, the personal voice assistant is constantly listening and waiting to be activated. Private content in speech may be maliciously extracted through automatic speech recognition (ASR) technology by some applications on phone devices. To guarantee that the recognized speech content is accurate, speech enhancement technology is used to denoise the input speech. Speech enhancement technology has developed rapidly along with deep neural networks (DNNs), but adversarial examples can cause DNNs to fail. Considering that the vulnerability of DNN can be used to protect the privacy in… More >

  • Open Access


    Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

    Xiaojun Zhu1,2,3, Heming Huang1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2155-2172, 2023, DOI:10.32604/cmes.2023.021453

    Abstract Recently, speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals. However, the training of Generative Adversarial Networks has such problems as convergence difficulty, model collapse, etc. In this work, an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed, and some improvements have been made in order to get faster convergence speed and better generated speech quality. Specifically, in the generator coding part, each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales; a gated More >

  • Open Access


    Speech Enhancement via Mask-Mapping Based Residual Dense Network

    Lin Zhou1,*, Xijin Chen1, Chaoyan Wu1, Qiuyue Zhong1, Xu Cheng2, Yibin Tang3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1259-1277, 2023, DOI:10.32604/cmc.2023.027379

    Abstract Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network (DNN). But the mapping-based methods only utilizes the phase of noisy speech, which limits the upper bound of speech enhancement performance. Masking-based methods need to accurately estimate the masking which is still the key problem. Combining the advantages of above two types of methods, this paper proposes the speech enhancement algorithm MM-RDN (masking-mapping residual dense network) based on masking-mapping (MM) and residual dense network (RDN). Using the logarithmic power spectrogram (LPS) of consecutive frames, MM estimates the ideal… More >

  • Open Access


    Real-Time Speech Enhancement Based on Convolutional Recurrent Neural Network

    S. Girirajan, A. Pandian*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1987-2001, 2023, DOI:10.32604/iasc.2023.028090

    Abstract Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output. In recent years, the need for speech enhancement has been increased due to challenges that occurred in various applications such as hearing aids, Automatic Speech Recognition (ASR), and mobile speech communication systems. Most of the Speech Enhancement research work has been carried out for English, Chinese, and other European languages. Only a few research works involve speech enhancement in Indian regional Languages. In this paper, we propose a two-fold architecture to perform speech enhancement for Tamil speech signal… More >

  • Open Access


    An Efficient Reference Free Adaptive Learning Process for Speech Enhancement Applications

    Girika Jyoshna1,*, Md. Zia Ur Rahman1, L. Koteswararao2

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3067-3080, 2022, DOI:10.32604/cmc.2022.020160

    Abstract In issues like hearing impairment, speech therapy and hearing aids play a major role in reducing the impairment. Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy. During the transmission of speech signals, several noise components contaminate the actual speech components. This paper addresses a new adaptive speech enhancement (ASE) method based on a modified version of singular spectrum analysis (MSSA). The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component. The MSSA adopts three key… More >

  • Open Access


    Speech Enhancement via Residual Dense Generative Adversarial Network

    Lin Zhou1,*, Qiuyue Zhong1, Tianyi Wang1, Siyuan Lu1, Hongmei Hu2

    Computer Systems Science and Engineering, Vol.38, No.3, pp. 279-289, 2021, DOI:10.32604/csse.2021.016524

    Abstract Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which… More >

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