Learning Embodied Semantics via Music and Dance Semiotic Correlations
This work addresses the challenge of capturing embodied cognition in music for applications like music-dance recommendation, but it appears incremental as it builds on existing embodied cognition perspectives.
The paper tackled the problem of learning embodied semantics by modeling correlations between music audio and dance video, and demonstrated that the model can effectively perform cross-modal retrieval between music and dance.
Music semantics is embodied, in the sense that meaning is biologically mediated by and grounded in the human body and brain. This embodied cognition perspective also explains why music structures modulate kinetic and somatosensory perception. We leverage this aspect of cognition, by considering dance as a proxy for music perception, in a statistical computational model that learns semiotic correlations between music audio and dance video. We evaluate the ability of this model to effectively capture underlying semantics in a cross-modal retrieval task. Quantitative results, validated with statistical significance testing, strengthen the body of evidence for embodied cognition in music and show the model can recommend music audio for dance video queries and vice-versa.