Cover song detection: From high scores to general classification
Cover song detection: From high scores to general classification
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DOI：
10.1109/icassp.2010.5496214
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Suman V. Ravuri;D. Ellis
中科院分区：
文献类型：
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作者：
Suman V. Ravuri;D. Ellis
现有的翻唱歌曲检测系统需要测试集中翻唱歌曲的数量的先验知识，以便识别参考歌曲的翻唱。我们描述了一个系统，不需要这样的先验知识。系统的输入是参考轨道和测试轨道，输出是输入是参考轨道和覆盖轨道还是参考轨道和非覆盖轨道的二元分类。该系统与最先进的检测器不同，计算多个输入特征，执行一种新型的测试歌曲标准化，以打击“冒名顶替者”的轨道，并使用支持向量机（SVM）或多层感知器（MLP）进行分类。在covers80测试集上，该系统的错误率为10%，而2007年LabROSA翻唱歌曲检测系统的错误率为21.3%。
Existing cover song detection systems require prior knowledge of the number of cover songs in a test set in order to identify cover(s) to a reference song. We describe a system that does not require such prior knowledge. The input to the system is a reference track and test track, and the output is a binary classification of whether the inputs are either a reference and a cover or a reference and a non-cover. The system differs from state-of-the-art detectors by calculating multiple input features, performing a novel type of test song normalization in order to combat against “impostor” tracks, and performing classification using either a support vector machine (SVM) or multi-layer perceptron (MLP). On the covers80 test set, the system achieves an equal error rate of 10%, compared to 21.3% achieved by the 2007 LabROSA cover song detection system.