1 citations · 1 across the 3 of their papers we have counts for
9 papers
Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training
Ryoto Ishizuka, Ryo Nishikimi, Eita Nakamura +1
This paper describes a neural drum transcription method that detects from music signals the onset times of drums at the level, where tatum times are assumed to be…
Non-Local Musical Statistics as Guides for Audio-to-Score Piano Transcription
Kentaro Shibata, Eita Nakamura, Kazuyoshi Yoshii
We present an automatic piano transcription system that converts polyphonic audio recordings into musical scores. This has been a long-standing problem of music information process…
Semi-supervised Neural Chord Estimation Based on a Variational Autoencoder with Latent Chord Labels and Features
Yiming Wu, Tristan Carsault, Eita Nakamura +1
This paper describes a statistically-principled semi-supervised method of automatic chord estimation (ACE) that can make effective use of music signals regardless of the availabili…
Multi-Step Chord Sequence Prediction Based on Aggregated Multi-Scale Encoder-Decoder Network
Tristan Carsault, Andrew McLeod, Philippe Esling +3
This paper studies the prediction of chord progressions for jazz music by relying on machine learning models. The motivation of our study comes from the recent success of neural ne…
Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions
Eita Nakamura, Kazuyoshi Yoshii
Most work on musical score models (a.k.a. musical language models) for music transcription has focused on describing the local sequential dependence of notes in musical scores and…
Statistical Learning and Estimation of Piano Fingering
Eita Nakamura, Yasuyuki Saito, Kazuyoshi Yoshii
Automatic estimation of piano fingering is important for understanding the computational process of music performance and applicable to performance assistance and education systems…