Pan Weihao, Jiao Jun, Zhou Xiaobo, Xu Zhengrong, Gu Lichuan, Zhu Cheng
College of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.
Sensors (Basel). 2024 Aug 10;24(16):5173. doi: 10.3390/s24165173.
In order to solve the problem of difficult separation of audio signals collected in pig environments, this study proposes an underdetermined blind source separation (UBSS) method based on sparsification theory. The audio signals obtained by mixing the audio signals of pigs in different states with different coefficients are taken as observation signals, and the mixing matrix is first estimated from the observation signals using the improved AP clustering method based on the "two-step method" of sparse component analysis (SCA), and then the audio signals of pigs are reconstructed by L1-paradigm separation. Five different types of pig audio are selected for experiments to explore the effects of duration and mixing matrix on the blind source separation algorithm by controlling the audio duration and mixing matrix, respectively. With three source signals and two observed signals, the reconstructed signal metrics corresponding to different durations and different mixing matrices perform well. The similarity coefficient is above 0.8, the average recovered signal-to-noise ratio is above 8 dB, and the normalized mean square error is below 0.02. The experimental results show that different audio durations and different mixing matrices have certain effects on the UBSS algorithm, so the recording duration and the spatial location of the recording device need to be considered in practical applications. Compared with the classical UBSS algorithm, the proposed algorithm outperforms the classical blind source separation algorithm in estimating the mixing matrix and separating the mixed audio, which improves the reconstruction quality.
为了解决猪舍环境中采集的音频信号分离困难的问题，本研究提出一种基于稀疏化理论的欠定盲源分离（UBSS）方法。将不同状态下猪的音频信号以不同系数混合得到的音频信号作为观测信号，首先基于稀疏分量分析（SCA）的“两步法”，利用改进的AP聚类方法从观测信号中估计混合矩阵，然后通过L1范数分离重建猪的音频信号。选取五种不同类型的猪音频进行实验，分别通过控制音频时长和混合矩阵来探究时长和混合矩阵对盲源分离算法的影响。对于三个源信号和两个观测信号，不同时长和不同混合矩阵对应的重建信号指标表现良好。相似系数高于0.8，平均恢复信噪比高于8 dB，归一化均方误差低于0.02。实验结果表明，不同的音频时长和不同的混合矩阵对UBSS算法有一定影响，因此在实际应用中需要考虑录音时长和录音设备的空间位置。与经典的UBSS算法相比，所提算法在估计混合矩阵和分离混合音频方面优于经典盲源分离算法，提高了重建质量。
