4 papers · 1 filter
Dance to Music Generation leveraging Pre-training with Unpaired data and Contrastive Alignment
Ryota Kimura, Sangheon Park, Natalia Polouliakh +1
Dance-to-music generation is a promising task for applications such as choreography support and automatic accompaniment, where temporal coordination between body movement and sound…
PF-D2M: A Pose-free Diffusion Model for Universal Dance-to-Music Generation
Jaekwon Im, Natalia Polouliakh, Taketo Akama
Dance-to-music generation aims to generate music that is aligned with dance movements. Existing approaches typically rely on body motion features extracted from a single human danc…
Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
Emilian Postolache, Natalia Polouliakh, Hiroaki Kitano +4
In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroen…
Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging
Taketo Akama, Hiroaki Kitano, Katsuhiro Takematsu +2
In the realm of music information retrieval, similarity-based retrieval and auto-tagging serve as essential components. Given the limitations and non-scalability of human supervisi…