Performance comparison of MUSIC-based sound localization methods on small humanoid under low SNR conditions
Performance comparison of MUSIC-based sound localization methods on small humanoid under low SNR conditions
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DOI：
10.1109/humanoids.2015.7363462
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Ryu Takeda;Kazunori Komatani
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文献类型：
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作者：
Ryu Takeda;Kazunori Komatani
我们专注于声源定位（SSL）问题的小/弱的声音记录的小型人形机器人，如Nao。这种机器人上的小声音的定位比具有大身体的机器人更困难，因为1）麦克风的数量和它们的位置受到限制，以及2）内部噪声和人类语音之间的信噪比（SNR）低。多信号分类（MUSIC）是一种很有前途的抗噪SSL方法，其特征向量分解过程有多种变体，如广义特征值分解（GEVD-MUSIC）和广义奇异值分解（GSVD-MUSIC）。然而，它们的性能受到噪声特性、麦克风数量以及它们在每个机器人身上的配置的严重影响。由于这些已被证实，只有在机器人与大型机构和许多麦克风，我们需要调查的变种执行更好的小型人形机器人与内部noise init. We还提出了另一种MUSIC基于变换导向矢量（TSV-MUSIC）作为一个新的GEVD-MUSIC的实现。与GEVD-MUSIC算法相比，TSV-MUSIC算法通过改变其矩阵相乘过程，降低了计算量。利用真实的重编码数据的实验结果表明，在低信噪比条件下，我们的TSV-MUSIC在定位正确性方面优于其他人约10个点。我们还通过模拟数据比较了基于MUSIC的SSL的特性和性能。
We focus on the sound source localization (SSL) problem of small/weak voices recorded by small humanoid robots, such as Nao. The localization of small voice on such robots is more difficult than those with large bodies because 1) the number of microphones and their positions are restricted and 2) the signal to noise ratio (SNR) between internal noise and human speech is low. Multiple Signal Classification (MUSIC) is a promising noise-robust SSL method, and it has several variants in its eigenvector decomposition process, such as generalized eigenvalue decomposition (GEVD-MUSIC) and generalized singular value decomposition (GSVD-MUSIC). However, their performances are seriously affected by noise properties, the number of microphone, and their configurations on each robot's body. Since these have been confirmed only on robots with large bodies and many microphones, we need to investigate which variants perform better on a small humanoid robot with internal noise in it. We furthermore propose another MUSIC based on transformed steering vector (TSV-MUSIC) as a new implementation of GEVD-MUSIC. The computational cost of TSV-MUSIC is reduced compared with GEVD-MUSIC by changing its matrix multiplication procedures. Experimental results using real recoded data showed that our TSV-MUSIC outperformed others in terms of localization correctness by about 10 points under low SNR condition. We also compared the properties and performances of MUSIC-based SSLs by using simulated data.