12 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…
Interpretable and Perceptually-Aligned Music Similarity with Pretrained Embeddings
Arhan Vohra, Taketo Akama
Perceptual similarity representations enable music retrieval systems to determine which songs sound most similar to listeners. State-of-the-art approaches based on task-specific tr…
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…
Self-supervised restoration of singing voice degraded by pitch shifting using shallow diffusion
Yunyi Liu, Taketo Akama
Pitch shifting has been an essential feature in singing voice production. However, conventional signal processing approaches exhibit well known trade offs such as formant shifts an…
Towards Realistic Synthetic Data for Automatic Drum Transcription
Pierfrancesco Melucci, Paolo Merialdo, Taketo Akama
Deep learning models define the state-of-the-art in Automatic Drum Transcription (ADT), yet their performance is contingent upon large-scale, paired audio-MIDI datasets, which are…