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  • Open Access


    Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification

    Chaosheng Tang1, Deepak Ranjan Nayak2, Shuihua Wang1,3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 299-313, 2020, DOI:10.32604/cmes.2020.011069

    Abstract Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical machine learning methods: multilayer perceptron (MLP), support vector machine (SVM), and least-square support vector machine (LS-SVM) approach and made a further optimization of the LS-SVM model via wavelet entropy. The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector More >

  • Open Access


    Cochlear Synaptopathy Following Noise Exposure in Guinea Pigs: Its Electrophysiological and Histological Assessments

    Parvane Mahdi1, Akram Pourbakht1,*, Vahid Pirhajati Mahabadi2, Alireza Karimi Yazdi3, Mahtab Rabbani Anari3, Mohammad Kamali4

    Sound & Vibration, Vol.54, No.3, pp. 163-177, 2020, DOI:10.32604/sv.2020.09880

    Abstract Exposure to high level of noise, may cause the permanent cochlear synaptic degeneration. In present study, a model of noise induced cochlear synaptopathy was established and the electrophysiological and histological metrics for its assessment was designed. 6 guinea pigs were subjected to a synaptopathic noise (octave band of 4 kHz at 104 dB SPL, for 2-h). The amplitude growth curve of Auditory Brainstem Response (ABR) wave-I and wave-III latency shift in presence of noise were calculated. These indexes were considered in pre-exposure, 1 day post exposure (1DPE), 1 week post exposure (1WPE) and 1 month… More >

  • Open Access


    Using Audiometric Data to Weigh and Prioritize Factors that Affect Workers’ Hearing Loss through Support Vector Machine (SVM) Algorithm

    Hossein ElahiShirvan1, MohammadReza Ghotbi-Ravandi2, Sajad Zare3,*, Mostafa Ghazizadeh Ahsaee4

    Sound & Vibration, Vol.54, No.2, pp. 99-112, 2020, DOI:10.32604/sv.2020.08839

    Abstract Workers’ exposure to excessive noise is a big universal work-related challenges. One of the major consequences of exposure to noise is permanent or transient hearing loss. The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’ hearing loss based using the Support Vector Machine (SVM) algorithm. This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran. The participating workers (n = 150) were divided into three groups of 50 based on the sound pressure level to which they were exposed (two experimental groups and… More >

  • Open Access


    Research on Nonlinear Frequency Compression Method of Hearing Aid with Adaptive Compression Ratio

    Xia Wang1, Hongming Shen1, Huawei Tao2, Ruiyu Liang3, Xing Deng4,5, Haijian Shao5, Li Zhao6,*, Cairong Zou6

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 551-567, 2019, DOI:10.32604/cmes.2019.06314

    Abstract To make full use of the residual audible frequency band of hearing-loss patients and improve the intelligibility of speech, an adaptive nonlinear frequency compression (NFC) algorithm is proposed, which amplifies signals below the cutoff frequency while compresses signals above the cutoff frequency. Firstly, high-frequency signals are decomposed to critical band signals according to the BARK scale. Secondly, the global compression ratio is determined according to the patient's cutoff frequency and maximum audible frequency. Thirdly, the sub-band compression ratio is adaptively determined based on the global compression ratio and normalized average energy of subband signals. Finally, More >

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