Music similarity search is useful for a variety of creative tasks such as
replacing one music recording with another recording with a similar "feel", a
common task in video editing. For this task, it is typically necessary to
define a similarity metric to compare one recording to another. Music
similarity, however, is hard to define and depends on multiple simultaneous
notions of similarity (i.e. genre, mood, instrument, tempo). While prior work
ignore this issue, we embrace this idea and introduce the concept of
multidimensional similarity and unify both global and specialized similarity
metrics into a single, semantically disentangled multidimensional similarity
metric. To do so, we adapt a variant of deep metric learning called conditional
similarity networks to the audio domain and extend it using track-based
information to control the specificity of our model. We evaluate our method and
show that our single, multidimensional model outperforms both specialized
similarity spaces and alternative baselines. We also run a user-study and show
that our approach is favored by human annotators as well.

本文介绍了一个新的多维相似度度量方法，通过深度度量学习和基于音轨的信息相结合，将全局和专业的相似性策略融合在了一起，使得音乐相似度搜索的结果更准确。作者在用户研究中证明了该方法具有显著性能优势。