一种利用分布式传声器阵列的声源三维定位方法

Three-dimensional sound source localization using distributed microphone arrays

为了提高噪声和混响条件下分布式传声器阵列进行声源定位的性能,提出一种利用空间稀疏性和压缩感知原理的声源三维定位方法。该方法首先通过两次离散余弦变换方式提取出声音信号特征,并用该特征来构建稀疏定位模型,以便能够综合利用语音信号的短时和长时特性,同时降低模型维数;然后利用在线字典学习技术动态调整字典,克服稀疏模型与实际信号之间的失配问题,增强稀疏定位模型的鲁棒性;进而提出一种改进的平滑l_0范数稀疏重构算法来进行声源位置解算,以提高低信噪比条件下的重构精度。仿真结果表明该方法不仅可以实现多目标定位,而且具有较强的抗噪声和抗混响能力.

To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments, a sound source localization method was proposed. This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing （CS） theory. In this method two-step discrete cosine transform （DCT）-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model. Moreover, an online dictionary learning （DL） method was used to dynamically adjust the dictionary for matching the changes of acoustic signals, and then the sparse solution could better represent location estimations. In addition, we proposed an improved approximate 10 norm minimization algorithm to enhance reconstruction performance for sparse signals in the low signal-noise ratio （SNR） condition. The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.