用于翻唱歌曲识别的每色度熵

A. Camarena-Ibarrola, Karina Figueroa, Hector Tejeda-Villela
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引用次数: 1

摘要

翻唱歌曲是对先前录制的音乐片段的演绎,翻唱歌曲识别是指自动识别一首歌在感知上与同一首歌的另一场表演相同,即使这首歌是在不同的地方演奏的,也许是由其他音乐家用他们自己的乐器演奏的。它是音频信号处理中一个非常有挑战性的问题,其表现形式在节奏、速度和仪器方面都有所不同,因此比传统的音频指纹识别问题要困难得多。我们从音频信号中提取特征,通过所有八度来测量由半音分组的信号的信息内容水平,我们确定每色度值的熵,然后使用动态规划技术来对齐呈现,因为它们在时间上有不同的演变,并且不会持续相同。我们的测试使用了两首钢琴曲的23次演奏,取得了优异的成绩。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Entropy per chroma for Cover song identification
A Cover song is a rendition of a previously recorded piece of music, cover song identification is about automatically recognizing a song as perceptually the same as another performance of the same song even thought it was played in a different place, perhaps by other musicians each with their own musical instruments. It is a challenging problem of great interest in audio-signal processing, renditions differ in rhythm, tempo, and instrumentation, so it is much more difficult than the classical audio-fingerprinting problem. We extract features from the audio-signal that measure the information content level of the signal grouped by semitones through all octaves, we determine the entropy per chroma value, then use dynamic programming techniques for aligning the renditions since they have different evolution in time and do not last the same. Our tests use 23 performances of two piano pieces and achieved excellent results.
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