137 citations · 311 across the 28 of their papers we have counts for
4 papers · 1 filter
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…