Instrument Identification in Polyphonic Music: Feature Weighting to Minimize Influence of Sound Overlaps
Instrument Identification in Polyphonic Music: Feature Weighting to Minimize Influence of Sound Overlaps
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DOI：
10.1155/2007/51979
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发表时间：
2007
影响因子：
1.9
通讯作者：
Tetsuro Kitahara;Masataka Goto;Kazunori Komatani;T. Ogata;HIroshi G. Okuno
中科院分区：
文献类型：
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作者：
Tetsuro Kitahara;Masataka Goto;Kazunori Komatani;T. Ogata;HIroshi G. Okuno
针对复调音乐中乐器识别中由于声音重叠而引起的特征变化问题，提出了一种新的解决方法。当多个乐器同时演奏时，它们的声音的分音（谐波分量）重叠和干扰，这使得声学特征不同于单声道声音。为了科普这个问题，我们根据重叠对特征的影响程度来对特征进行加权。首先，我们定量评估重叠对每个特征的影响，作为从复调声音获得的训练数据分布中的类内方差与类间方差的比率。然后，我们使用加权混合生成特征轴，通过线性判别分析最大限度地减少影响。此外，我们提高了乐器识别使用音乐的背景。实验结果表明，使用特征加权和音乐上下文的识别率分别为84.1二重奏，77.6三重奏，和72.3四重奏;那些没有使用任何一个分别为53.4，49.6，和46.5，分别。
We provide a new solution to the problem of feature variations caused by the overlapping of sounds in instrument identification in polyphonic music. When multiple instruments simultaneously play, partials (harmonic components) of their sounds overlap and interfere, which makes the acoustic features different from those of monophonic sounds. To cope with this, we weight features based on how much they are affected by overlapping. First, we quantitatively evaluate the influence of overlapping on each feature as the ratio of the within-class variance to the between-class variance in the distribution of training data obtained from polyphonic sounds. Then, we generate feature axes using a weighted mixture that minimizes the influence via linear discriminant analysis. In addition, we improve instrument identification using musical context. Experimental results showed that the recognition rates using both feature weighting and musical context were 84.1 for duo, 77.6 for trio, and 72.3 for quartet; those without using either were 53.4, 49.6, and 46.5, respectively.