融合共点声强特征的掩蔽加权声源定位方法

Mask weighted sound source localization by incorporating co-located sound intensity features

提出了一种适用于小尺寸传声器阵列的掩蔽加权深度学习声源定位方法。该方法在功率谱特征提取的基础上,通过解决声强特征提取中的非共点问题,进一步融合了不同方向上的共点声强特征,有效提高了混响噪声环境条件下的时频掩蔽准确性和定位性能。该方法无需峰值搜索,计算复杂度较小。仿真和实测实验结果表明,所提方法在混响噪声环境下的定位性能优于现有的掩蔽加权定位方法,且在信噪比较低、混响时间较长条件下优势更为明显,实测实验的声源定位精度超过现有方法 6.67%~10.00%。

A mask-weighted deep learning sound source localization method is proposed for small-sized microphone arrays.Based on the power spectrum feature extraction,the co-located sound intensity feature in different directions is further integrated to solve the non-co-located problem in the sound intensity feature extraction,effectively improving the accuracy of time-frequency masking and the localization performance under reverberant and noisy environments.Furthermore,peak searching is not required,and low computational complexity is maintained.Simulation and experimental results show that the localization performance of proposed method outperforms the existing mask-weighted localization methods under reverberant and noisy environments,with an advantage becoming more pronounced under low signal-to-noise ratios and long reverberation times.The accuracy of sound source localization was found to exceed existing methods by 6.67%~10.00%in real-world experiments.