基于人耳听觉特性的非线性频率压缩方法

    Nonlinear Frequency Compression Method Based on Human Auditory Characteristics

    • 摘要: 针对数字助听器中现有的响度补偿算法无法满足高频敏感度较低的听障患者对言语的可懂度及舒适度需求的问题,结合耳蜗特性提出了一种基于人耳听觉特性的非线性频率压缩方法.首先,根据语音中前2个共振峰对元音识别起关键作用的特点,先对语音信号进行共振峰检测,保留前2个共振峰处于非压缩频段,确定频率的总压缩比;然后,将频域转换到Mel域,利用Mel域内人耳对Mel频率的感知呈近似线性这一特点,计算Mel域内各个子带占总的待压缩频带的比例,以此作为频域内各子带压缩比的分配依据,在频域内进行非线性频率压缩;最后,对移频压缩后的对于听障患者来说较为敏感的中低频信号依据听障患者的听力曲线进行多通道的响度补偿.实验结果表明,该算法相对于对比算法来说,更好地保护了语音的谱峰比,提高了听障患者的舒适度及可懂度.

       

      Abstract: To solve the problem that the existing loudness compensation algorithm in digital hearing AIDS cannot meet the speech intelligibility and audibility of hearing impaired patients with low high frequency sensitivity, a nonlinear frequency compression method of digital hearing aids based on the auditory characteristics of human ear and cochlea was proposed. First, according to the characteristic that the first two formants in speech play a key role in vowel recognition, and the formant detection of speech signal was carried out. The first two formants were kept in the uncompressed frequency band to determine the total compression ratio of frequency. Then, the frequency domain was converted to the Mel domain, and the ratio of each subband in the Mel domain to the total frequency band to be compressed was calculated by using the feature that the human ear in the Mel domain has an approximate linear perception of the Mel frequency. As the basis for the distribution of the compression ratio of each subband in the frequency domain, nonlinear frequency compression was performed in the frequency domain. Finally, multi-channel loudness compensation was performed for the low-and medium-frequency signals that are sensitive to hearing impairment after frequency shift compression according to the hearing curve of hearing impairment patients. Results show that compared with the comparison algorithm, the proposed algorithm can better protect the spectral peak ratio of speech, and improve the audibility and intelligibility of patients with hearing impairment.

       

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