Abstract: Extraction of the signals, which can represent sound source information and meanwhile include noise signals, from acoustic sensors is the key technique for sound emission monitoring. To eliminate the noise efficiently and raise the signaltonoise ratio, this paper brings up a dualadaptive noisecancellation algorithm, which includes adaptive subband decomposition algorithm and adaptive noise cancellation algorithm. First of all, to reduce the complexity in computation and realize parallel algorithm, the method of the subband decomposition, which matches the noise powerspectrum density, is adopted for nonuniform signal subband decomposition. Then, to save the computer time, the effective filtering is performed according to the distribution of noise in the subband. The subbands with lownoise or essentially without noise do not need filtering, while the adaptive filtering algorithm is used for the other subbands. The simulation shows that this method can greatly save computer time in comparison with the traditional adaptive noise cancellation method with uniform subbands, and the effect of filtering has been improved.

Key words: acoustics, adaptive subband decomposition, adaptive noise cancellation, powerspectrum density

摘要： 为有效剔除噪声，提高信噪比，提出一种基于双自适应的噪声抵消算法，包括自适应子带分解算法和自适应噪声抵消算法两部分。采用子带分解与噪声功率谱密度匹配的方法来对信号进行非均匀子带分解，根据噪声在子带中的分布进行有效滤波，对低噪或基本上无噪的子带不滤波，而对其它子带采用自适应滤波的算法。仿真对比表明，与传统的均匀子带自适应噪声抵消相比，计算量大大减小，其滤波效果也得到一定的改善。

关键词: 声学, 自适应子带分解, 自适应抵消, 功率谱密度