Spatial Cepstrum as a Spatial Feature Using a Distributed Microphone Array for Acoustic Scene Analysis
Spatial Cepstrum as a Spatial Feature Using a Distributed Microphone Array for Acoustic Scene Analysis
复制标题
空间倒谱作为空间特征，使用分布式麦克风阵列进行声学场景分析
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Nobutaka Ono
中科院分区：
文献类型：
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作者：
Keisuke Imoto;Nobutaka Ono
本文针对利用分布式麦克风阵列的空间信息进行声场景分析的问题，提出了一种稳健有效的空间倒谱方法。在我们的方法中，类似于倒谱，这是广泛使用的频谱特征，在多通道观测的幅度的对数转换为一个特征向量的线性正交变换。该线性正交变换通常通过主成分分析（PCA）来实现。此外，我们还表明，对于具有各向同性声场的圆形对称麦克风布置，PCA与逆离散傅立叶变换相同，并且空间倒谱恰好对应于倒谱。所提出的方法不需要麦克风的位置，并且对通道的同步失配具有鲁棒性，从而确保其适用于分布式麦克风阵列。使用实际的环境声音得到的实验结果验证了我们的方法的有效性，即使当使用比原来的一个更小的特征维数，这是通过PCA降维实现的。此外，实验结果还表明，该方法的鲁棒性是令人满意的观测通道同步失配。
In this paper, with the aim of using the spatial information obtained from a distributed microphone array employed for acoustic scene analysis, we propose a robust and efficient method, which is called the spatial cepstrum. In our approach, similarly to the cepstrum, which is widely used as a spectral feature, the logarithm of the amplitude in multichannel observation is converted to a feature vector by a linear orthogonal transformation. This linear orthogonal transformation is achieved by principal component analysis (PCA) in general. Moreover, we also show that for a circularly symmetric microphone arrangement with an isotropic sound field, PCA is identical to the inverse discrete Fourier transform and the spatial cepstrum exactly corresponds to the cepstrum. The proposed approach does not require the positions of the microphones and is robust against the synchronization mismatch of channels, thus ensuring its suitability for use with a distributed microphone array. Experimental results obtained using actual environmental sounds verify the validity of our approach even when a smaller feature dimension than the original one is used, which is achieved by dimensionality reduction through PCA. Additionally, experimental results also indicate that the robustness of the proposed method is satisfactory for observations that have the synchronization mismatch of channels.
影响因子：
14.9
作者：
Barchiesi, Daniele;Giannoulis, Dimitrios;Plumbley, Mark D.
通讯作者：
Plumbley, Mark D.