8 papers
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…
Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity
Shogo Noguchi, Taketo Akama, Tai Nakamura +2
During music listening, cortical activity encodes both acoustic and expectation-related information. Prior work has shown that ANN representations resemble cortical representations…
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…
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…