A filter algorithm based on cochlear mechanics and neuron filter mechanism is proposed from the view point of vibration. It helps to solve the problem that the non-linear amplification is rarely considered in studying the auditory filters. A cochlear mechanical transduction model is built to illustrate the audio signals processing procedure in cochlea, and then the neuron filter mechanism is modeled to indirectly obtain the outputs with the cochlear properties of frequency tuning and non-linear amplification. The mathematic description of the proposed algorithm is derived by the two models. The parameter space, the parameter selection rules and the error correction of the proposed algorithm are discussed. The unit impulse responses in the time domain and the frequency domain are simulated and compared to probe into the characteristics of the proposed algorithm. Then a 24-channel filter bank is built based on the proposed algorithm and applied to the enhancements of the audio signals. The experiments and comparisons verify that, the proposed algorithm can effectively divide the audio signals into different frequencies, significantly enhance the high frequency parts, and provide positive impacts on the performance of speech enhancement in different noise environments, especially for the babble noise and the volvo noise.

从振动的角度出发，提出了一种基于耳蜗力学和神经元滤波机制的滤波算法。它有助于解决在研究听觉滤波器时很少考虑非线性放大的问题。建立耳蜗机械转导模型来说明耳蜗中音频信号的处理过程，然后对神经元滤波机制进行建模，以间接获得具有频率调谐和非线性放大的耳蜗特性的输出。这两个模型对所提算法进行了数学描述。讨论了所提出算法的参数空间，参数选择规则和纠错。仿真并比较了时域和频域的单位冲激响应，以探究所提算法的特点。然后，基于提出的算法建立了一个24通道的滤波器组，并将其应用于音频信号的增强。实验和比较结果表明，所提算法可以有效地将音频信号划分为不同的频率，显着增强高频部分，对不同噪声环境下的语音增强性能产生积极影响，特别是对于bble声和沃尔沃噪音。