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Compressive Coding of Stereo Audio Signals Extracting Sparseness among Sound Sources with Independent Component Analysis

机译:立体声音频信号的压缩编码提取具有独立分析的声源间稀疏性

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In this paper we propose a new compressive coding method of stereo audio signals extracting sparseness among sound sources by using independent component analysis. Some researchers have proposed a compressive coding method of multi-channel audio called binaural cue coding (BCC), and the ISO/MPEG standardization group discusses standard of next generation audio based on BCC. BCC has an underlying model assuming existence of only a single sound source in each subband of the multi-channel audio signal. Mismatch of this model often occurs and as a result quality of reconstructed multi-channel signal degrades. To extract the time-frequency grids where only a single source exists, we apply independent component analysis (ICA) to stereo signals. Using this analysis, a single dominant source can be chosen efficiently in each of frequency bins. In addition, transfer functions to reconstruct stereo signal from the dominant source is also extracted by ICA. Experiments based on both objective and subjective evaluations ascertains efficiency of the proposed method.
机译:在本文中,我们通过使用独立的分量分析提出了一种新的立体声音频信号的压缩编码方法,立体声音频信号提取声源之间的稀疏性。一些研究人员提出了一种名为双通道音频的压缩编码方法,称为双耳芯片编码(BCC),并且ISO / MPEG标准化组基于BCC讨论了下一代音频的标准。 BCC具有底层模型,假设仅存在多通道音频信号的每个子带中的单个声源。这种模型的不匹配通常发生,并且由于重建的多通道信号的质量降低。为了提取仅存在单个源的时频网格,我们将独立的分量分析(ICA)应用于立体声信号。使用该分析,可以在每个频率箱中有效地选择单个主导源。另外,ICA也提取了从主导源重建立体声信号的传递函数。基于目标和主观评估的实验确定提出的方法的效率。

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