32 citations · 106 across the 16 of their papers we have counts for
16 papers
Dynamic cluster structure and predictive modelling of music creation style distributions
Rajsuryan Singh, Eita Nakamura
We investigate the dynamics of music creation style distributions to understand cultural evolution involving advanced intelligence. Using statistical modelling methods and several…
Conjugate Distribution Laws in Cultural Evolution via Statistical Learning
Eita Nakamura
Many cultural traits characterizing intelligent behaviors are now thought to be transmitted through statistical learning, motivating us to study its effects on cultural evolution.…
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…
Statistical Evolutionary Laws in Music Styles
Eita Nakamura, Kunihiko Kaneko
If a cultural feature is transmitted over generations and exposed to stochastic selection when spreading in a population, its evolution may be governed by statistical laws and be p…
Statistical Piano Reduction Controlling Performance Difficulty
Eita Nakamura, Kazuyoshi Yoshii
We present a statistical-modelling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies…
Generative Statistical Models with Self-Emergent Grammar of Chord Sequences
Hiroaki Tsushima, Eita Nakamura, Katsutoshi Itoyama +1
Generative statistical models of chord sequences play crucial roles in music processing. To capture syntactic similarities among certain chords (e.g. in C major key, between G and…
Note Value Recognition for Piano Transcription Using Markov Random Fields
Eita Nakamura, Kazuyoshi Yoshii, Simon Dixon
This paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because p…
Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices
Eita Nakamura, Kazuyoshi Yoshii, Shigeki Sagayama
In a recent conference paper, we have reported a rhythm transcription method based on a merged-output hidden Markov model (HMM) that explicitly describes the multiple-voice structu…
Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips
Tomohiko Nakamura, Eita Nakamura, Shigeki Sagayama
This paper discusses real-time alignment of audio signals of music performance to the corresponding score (a.k.a. score following) which can handle tempo changes, errors and arbitr…
Outer-Product Hidden Markov Model and Polyphonic MIDI Score Following
Eita Nakamura, Tomohiko Nakamura, Yasuyuki Saito +2
We present a polyphonic MIDI score-following algorithm capable of following performances with arbitrary repeats and skips, based on a probabilistic model of musical performances. I…
A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments
Eita Nakamura, Nobutaka Ono, Shigeki Sagayama +1
We study indeterminacies in realization of ornaments and how they can be incorporated in a stochastic performance model applicable for music information processing such as score-pe…