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MUSIPER: a system for modeling music similarity perception based on objective feature subset selection

机译：MUSIPER：一种基于客观特征子集选择的音乐相似性感知建模系统

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摘要

We explore the use of objective audio signal features to model the individualized (subjective) perception of similarity between music files. We present MUSIPER, a content-based music retrieval system which constructs music similarity perception models of its users by associating different music similarity measures to different users. Specifically, a user-supplied relevance feedback procedure and related neural network-based incremental learning allows the system to determine which subset of a set of objective features approximates more accurately the subjective music similarity perception of a specific user. Our implementation and evaluation of MUSIPER verifies the relation between subsets of objective features and individualized music similarity perception and exhibits significant improvement in individualized perceived similarity in subsequent music retrievals.
机译：我们探索使用客观音频信号功能来建模音乐文件之间相似性的个体化（主观）感知。我们介绍了MUSIPER，这是一个基于内容的音乐检索系统，它通过将不同的音乐相似性度量与不同的用户相关联来构造其用户的音乐相似性感知模型。具体而言，用户提供的相关性反馈过程和相关的基于神经网络的增量学习使系统能够确定一组目标特征的哪个子集更准确地近似特定用户的主观音乐相似性感知。我们对MUSIPER的实施和评估验证了目标特征子集与个性化音乐相似性感知之间的关系，并在随后的音乐检索中展现了个性化感知相似性的显着改善。

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