4 papers
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…
Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
Taketo Akama, Zhuohao Zhang, Tsukasa Nagashima +3
Art has long played a profound role in shaping human emotion, cognition, and behavior. While visual arts such as painting and architecture have been studied through eye tracking, r…
SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
Taketo Akama, Akima Connelly, Shun Minamikawa +1
EEG recordings are inherently contaminated by artifacts such as ocular, muscular, and environmental noise, which obscure neural activity and complicate preprocessing. Artifact clas…
Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings
Taketo Akama, Zhuohao Zhang, Pengcheng Li +4
Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identi…