3 citations · 6 across the 19 of their papers we have counts for
21 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…
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…
Music Foundation Model as Generic Booster for Music Downstream Tasks
WeiHsiang Liao, Yuhta Takida, Yukara Ikemiya +13
We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation…
Annotation-Free MIDI-to-Audio Synthesis via Concatenative Synthesis and Generative Refinement
Osamu Take, Taketo Akama
Recent MIDI-to-audio synthesis methods using deep neural networks have successfully generated high-quality, expressive instrumental tracks. However, these methods require MIDI anno…
A Preliminary Investigation on Flexible Singing Voice Synthesis Through Decomposed Framework with Inferrable Features
Lester Phillip Violeta, Taketo Akama
We investigate the feasibility of a singing voice synthesis (SVS) system by using a decomposed framework to improve flexibility in generating singing voices. Due to data-driven app…
Music Proofreading with RefinPaint: Where and How to Modify Compositions given Context
Pedro Ramoneda, Martin Rocamora, Taketo Akama
Autoregressive generative transformers are key in music generation, producing coherent compositions but facing challenges in human-machine collaboration. We propose RefinPaint, an…
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…
A Computational Analysis of Lyric Similarity Perception
Haven Kim, Taketo Akama
In musical compositions that include vocals, lyrics significantly contribute to artistic expression. Consequently, previous studies have introduced the concept of a recommendation…
HyperGANStrument: Instrument Sound Synthesis and Editing with Pitch-Invariant Hypernetworks
Zhe Zhang, Taketo Akama
GANStrument, exploiting GANs with a pitch-invariant feature extractor and instance conditioning technique, has shown remarkable capabilities in synthesizing realistic instrument so…
Annotation-free Automatic Music Transcription with Scalable Synthetic Data and Adversarial Domain Confusion
Gakusei Sato, Taketo Akama
Automatic Music Transcription (AMT) is a vital technology in the field of music information processing. Despite recent enhancements in performance due to machine learning technique…
Automatic Piano Transcription with Hierarchical Frequency-Time Transformer
Keisuke Toyama, Taketo Akama, Yukara Ikemiya +3
Taking long-term spectral and temporal dependencies into account is essential for automatic piano transcription. This is especially helpful when determining the precise onset and o…
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…
GANStrument: Adversarial Instrument Sound Synthesis with Pitch-invariant Instance Conditioning
Gaku Narita, Junichi Shimizu, Taketo Akama
We propose GANStrument, a generative adversarial model for instrument sound synthesis. Given a one-shot sound as input, it is able to generate pitched instrument sounds that reflec…
A Contextual Latent Space Model: Subsequence Modulation in Melodic Sequence
Taketo Akama
Some generative models for sequences such as music and text allow us to edit only subsequences, given surrounding context sequences, which plays an important part in steering gener…