Search Results for author: Eita Nakamura

Found 13 papers, 5 papers with code

Conjugate Distribution Laws in Cultural Evolution via Statistical Learning

no code implementations2 Feb 2021 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.

Physics and Society Biological Physics

Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training

no code implementations8 Oct 2020 Ryoto Ishizuka, Ryo Nishikimi, Eita Nakamura, Kazuyoshi Yoshii

This paper describes a neural drum transcription method that detects from music signals the onset times of drums at the $\textit{tatum}$ level, where tatum times are assumed to be estimated in advance.

Drum Transcription Language Modelling

Multi-Step Chord Sequence Prediction Based on Aggregated Multi-Scale Encoder-Decoder Network

1 code implementation12 Nov 2019 Tristan Carsault, Andrew McLeod, Philippe Esling, Jérôme Nika, Eita Nakamura, Kazuyoshi Yoshii

In this paper, we postulate that this comes from the multi-scale structure of musical information and propose new architectures based on an iterative temporal aggregation of input labels.

Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions

no code implementations18 Aug 2019 Eita Nakamura, Kazuyoshi Yoshii

Focusing on rhythm, we formulate several classes of Bayesian Markov models of musical scores that describe repetitions indirectly using the sparse transition probabilities of notes or note patterns.

Language Modelling Music Transcription

Statistical Learning and Estimation of Piano Fingering

no code implementations23 Apr 2019 Eita Nakamura, Yasuyuki Saito, Kazuyoshi Yoshii

We find that the methods based on high-order HMMs outperform the other methods in terms of estimation accuracies.

Statistical Piano Reduction Controlling Performance Difficulty

no code implementations15 Aug 2018 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.

Note Value Recognition for Piano Transcription Using Markov Random Fields

no code implementations23 Mar 2017 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.

Music Transcription

Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices

no code implementations29 Jan 2017 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 structure of polyphonic music.

Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips

1 code implementation24 Dec 2015 Tomohiko Nakamura, Eita Nakamura, Shigeki Sagayama

We confirmed real-time operation of the algorithms with music scores of practical length (around 10000 notes) on a modern laptop and their tracking ability to the input performance within 0. 7 s on average after repeats/skips in clarinet performance data.

A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments

1 code implementation8 Apr 2014 Eita Nakamura, Nobutaka Ono, Shigeki Sagayama, Kenji Watanabe

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-performance matching.

Outer-Product Hidden Markov Model and Polyphonic MIDI Score Following

1 code implementation8 Apr 2014 Eita Nakamura, Tomohiko Nakamura, Yasuyuki Saito, Nobutaka Ono, Shigeki Sagayama

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.

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