Taketo Akama — accepted AI-conference papers

2025

Naturalistic Music Decoding from EEG Data via Latent Diffusion Models

Emilian Postolache, Natalia Polouliakh, Hiroaki Kitano, Akima Connelly, Emanuele Rodolà, Luca Cosmo +1

ICASSP 2025accepted

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 electroencephalogram (EEG) recordings. Unlike simpler music with limited timbres, such as MIDI-generated tunes or monophonic piec…

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2024

Hyperganstrument: Instrument Sound Synthesis and Editing With Pitch-Invariant Hypernetworks

Zhe Zhang, Taketo Akama

ICASSP 2024accepted

GANStrument, exploiting GANs with a pitch-invariant feature extractor and instance conditioning technique, has shown remarkable capabilities in synthesizing realistic instrument sounds. To further improve the reconstruction ability and pitch accuracy to enhance the editability of user-provided sound…

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2023

GANStrument: Adversarial Instrument Sound Synthesis with Pitch-Invariant Instance Conditioning

Gaku Narita, Junichi Shimizu, Taketo Akama

ICASSP 2023accepted

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 reflect the timbre of the input within an interactive time. By exploiting instance conditioning, GANStrument achieves better f…

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