no code implementations • 2 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
no code implementations • 8 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.
1 code implementation • 14 May 2020 • Yiming Wu, Tristan Carsault, Eita Nakamura, Kazuyoshi Yoshii
In contrast, we propose a unified generative and discriminative approach in the framework of amortized variational inference.
1 code implementation • 12 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.
no code implementations • 18 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.
no code implementations • 23 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.
no code implementations • 15 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.
no code implementations • 7 Aug 2017 • Hiroaki Tsushima, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii
Generative statistical models of chord sequences play crucial roles in music processing.
no code implementations • 23 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.
no code implementations • 29 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.
1 code implementation • 24 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.
1 code implementation • 8 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.
1 code implementation • 8 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.