Search Results for author: Taketo Akama

Found 7 papers, 1 papers with code

A Computational Analysis of Lyric Similarity Perception

no code implementations2 Apr 2024 Haven Kim, Taketo Akama

In musical compositions that include vocals, lyrics significantly contribute to artistic expression.

Recommendation Systems

HyperGANStrument: Instrument Sound Synthesis and Editing with Pitch-Invariant Hypernetworks

no code implementations9 Jan 2024 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 sounds.

Annotation-free Automatic Music Transcription with Scalable Synthetic Data and Adversarial Domain Confusion

no code implementations16 Dec 2023 Gakusei Sato, Taketo Akama

To tackle this issue, we propose a transcription model that does not require any MIDI-audio paired data through the utilization of scalable synthetic audio for pre-training and adversarial domain confusion using unannotated real audio.

Music Transcription

Automatic Piano Transcription with Hierarchical Frequency-Time Transformer

1 code implementation10 Jul 2023 Keisuke Toyama, Taketo Akama, Yukara Ikemiya, Yuhta Takida, Wei-Hsiang Liao, Yuki Mitsufuji

This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content.

Music Transcription

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

no code implementations10 Nov 2022 Gaku Narita, Junichi Shimizu, Taketo Akama

In addition, we introduce an adversarial training scheme for a pitch-invariant feature extractor that significantly improves the pitch accuracy and timbre consistency.

A Contextual Latent Space Model: Subsequence Modulation in Melodic Sequence

no code implementations23 Nov 2021 Taketo Akama

We propose a contextual latent space model (CLSM) in order for users to be able to explore subsequence generation with a sense of direction in the generation space, e. g., interpolation, as well as exploring variations -- semantically similar possible subsequences.

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