希尔伯特黄变换在电子耳蜗语音处理算法中的应用

The Application of Hilbert-Huang Transform in Speech Coding Algorithm for Cochlear implants

为了提高噪声环境下的语音、声调语音以及音乐的识别水平,基于希尔伯特黄变换提出一种新的电子耳蜗语音编码策略,利用经验模态分解和希尔伯特变换提取语音的幅度瞬时幅度和瞬时频率,经滤波、调制等处理算法获取表征语音的精细结构进而合成刺激信号。以Matlab软件为平台对提出的算法和传统的连续间隔采样以及幅频联合编码算法进行仿真,分别处理50组语音测听材料并合成相应的语音信号。结果显示新算法合成的语音信号与原始信号的相关系数高于另外两种算法得到的相关系数,从而表明新算法可以保留更多原始语音的信息。

To enhance speech recognition in realistic listening environment, as well as tonal language and music perception, a new speech coding strategy based on Hilbert Huang transform was presented. Instantaneous frequency and instantaneous amplitude which reflect speech contents, speech rhythms and tones are derived from original speech signal through empirical mode decomposition and the Hilbert transform to synthesize stimulating pulses. The presented new speech coding algorithm, continuous interleaved sampling, and frequency amplitude modulation encoding strategies were simulated by Matlab and synthesized signals of 50 Mandarin speech test materials are correlation analyzed between original signals. Compared to other two strategies, the presented new strategy obtains the highest correlation coefficient between synthesized signal and that of original speech, which indicates it could keep more information of the original speech signal than other two strategies.