3 papers
q-bio.NC2024
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…
cs.SD2024
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…
cs.SD2023
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…