Objective measures based on neural networks for hearing loss compensation techniques

A. Tungthangthum, J. C. Rutledge
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Abstract

An objective measures system has been developed to predict the results of subject-based tests for sensorineural hearing loss compensation techniques. Parameters related to the loudness level of the compensated speech signal are extracted from its frequency spectrum. These parameters are then used to train a neural network based phoneme classifier. Good prediction results have been achieved for two hearing impaired subjects.<>
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基于神经网络客观测量的听力损失补偿技术
一个客观的测量系统已经开发,以预测受试者测试的结果感音神经性听力损失补偿技术。从被补偿语音信号的频谱中提取与其响度级相关的参数。然后使用这些参数来训练基于音素分类器的神经网络。对两名听力受损受试者的预测结果良好
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Multidimensional scaling analysis of head-related transfer functions Robust adaptive processing of microphone array data for hearing aids Local silencing of room acoustic noise using broadband active noise control Computationally efficient compression of audio signals by means of RIQ-DPCM A simplified source/filter model for percussive sounds
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