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Semantic embeddings play a crucial role in natural language-based informationretrieval. Embedding models represent words and contexts as vectors whosespatial configuration is derived from the distribution of words in large textcorpora. While such representations are generally very powerful, they mightfail to account for fine-grained domain-specific nuances. In this article, weinvestigate this uncertainty for the domain of characterizations of expressivepiano performance. Using a music research dataset of free text performancecharacterizations and a follow-up study sorting the annotations into clusters,we derive a ground truth for a domain-specific semantic similarity structure.We test five embedding models and their similarity structure for correspondencewith the ground truth. We further assess the effects of contextualizingprompts, hubness reduction, cross-modal similarity, and k-means clustering. Thequality of embedding models shows great variability with respect to this task;more general models perform better than domain-adapted ones and the best modelconfigurations reach human-level agreement.

Are we describing the same sound? An analysis of word embedding spaces of expressive piano performance