Music similarity is an essential aspect of music retrieval, recommendation systems, and music analysis. Moreover, similarity is of vital interest for music experts, as it allows studying analogies and influences among composers and historical periods. Current approaches to musical similarity rely mainly on symbolic content, which can be expensive to produce and is not always readily available. Conversely, approaches using audio signals typically fail to provide any insight about the reasons behind the observed similarity. This research addresses the limitations of current approaches by focusing on the study of musical similarity using both symbolic and audio content. The aim of this research is to develop a fully explainable and interpretable system that can provide end-users with more control and understanding of music similarity and classification systems.

翻译：音乐相似度是音乐检索、推荐系统及音乐分析的核心要素。此外，相似度对于音乐专家具有关键价值，因其能揭示作曲家与历史时期之间的类比关系及相互影响。当前音乐相似度方法主要依赖符号化内容，此类内容的生成成本较高且难以实时获取。相比之下，基于音频信号的方法通常无法为观测到的相似性提供合理解释。本研究聚焦于符号化内容与音频内容的双模态音乐相似度分析，旨在突破现有方法的局限性。研究目标在于构建一个完全可解释与可阐释的系统，使终端用户能够更自主地掌控并理解音乐相似度及分类机制。