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ORCIDEita Nakamura
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2020 – today
- 2024
[c43]Fabian C. Moss, Eita Nakamura:
Modeling the Evolution of Harmony in Popular Music from Different Cultural Contexts. CHR 2024: 137-152- [i14]Francesco Foscarin, Emmanouil Karystinaios, Eita Nakamura, Gerhard Widmer:
Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving. CoRR abs/2407.21030 (2024) - 2023
- [c42]Daichi Kamakura, Eita Nakamura, Kazuyoshi Yoshii:
CTC2: End-to-End Drum Transcription Based on Connectionist Temporal Classification With Constant Tempo Constraint. APSIPA ASC 2023: 158-164 - [c41]Eita Nakamura, Yasuyuki Saito:
Evolutionary Analysis and Cultural Transmission Models of Color Style Distributions in Painting Arts. APSIPA ASC 2023: 506-513 - [c40]Tengyu Deng, Eita Nakamura, Kazuyoshi Yoshii:
Audio-to-Score Singing Transcription Based on Joint Estimation of Pitches, Onsets, and Metrical Positions With Tatum-Level CTC Loss. APSIPA ASC 2023: 583-590 - [c39]Norihiro Kato, Eita Nakamura, Kyoko Mine, Orie Doeda, Masanao Yamada:
Computational Analysis of Audio Recordings of Piano Performance for Automatic Evaluation. EC-TEL 2023: 586-592 - [c38]Moyu Terao, Eita Nakamura, Kazuyoshi Yoshii:
Neural Band-to-Piano Score Arrangement with Stepless Difficulty Control. ICASSP 2023: 1-5 - 2022
- [c37]Tengyu Deng, Eita Nakamura, Kazuyoshi Yoshii:
End-to-End Lyrics Transcription Informed by Pitch and Onset Estimation. ISMIR 2022: 633-639 - [c36]Florian Thalmann, Eita Nakamura, Kazuyoshi Yoshii:
Tracking the Evolution of a Band's Live Performances over Decades. ISMIR 2022: 850-857 - [c35]Pedro Ramoneda, Dasaem Jeong, Eita Nakamura, Xavier Serra, Marius Miron:
Automatic Piano Fingering from Partially Annotated Scores using Autoregressive Neural Networks. ACM Multimedia 2022: 6502-6510 - 2021
- [j11]Kentaro Shibata, Eita Nakamura, Kazuyoshi Yoshii:
Non-local musical statistics as guides for audio-to-score piano transcription. Inf. Sci. 566: 262-280 (2021) - [j10]Eita Nakamura, Kazuyoshi Yoshii:
Musical rhythm transcription based on Bayesian piece-specific score models capturing repetitions. Inf. Sci. 572: 482-500 (2021) - [c34]Yuki Hiramatsu, Go Shibata, Ryo Nishikimi, Eita Nakamura, Kazuyoshi Yoshii:
Statistical Correction of Transcribed Melody Notes Based on Probabilistic Integration of a Music Language Model and a Transcription Error Model. ICASSP 2021: 256-260 - [c33]Yuki Hiramatsu, Eita Nakamura, Kazuyoshi Yoshii:
Joint Estimation of Note Values and Voices for Audio-to-Score Piano Transcription. ISMIR 2021: 278-284 - 2020
- [j9]Eita Nakamura
, Yasuyuki Saito, Kazuyoshi Yoshii:
Statistical learning and estimation of piano fingering. Inf. Sci. 517: 68-85 (2020) - [j8]Hiroaki Tsushima, Eita Nakamura, Kazuyoshi Yoshii:
Bayesian Melody Harmonization Based on a Tree-Structured Generative Model of Chord Sequences and Melodies. IEEE ACM Trans. Audio Speech Lang. Process. 28: 1644-1655 (2020) - [j7]Ryo Nishikimi, Eita Nakamura, Masataka Goto, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Bayesian Singing Transcription Based on a Hierarchical Generative Model of Keys, Musical Notes, and F0 Trajectories. IEEE ACM Trans. Audio Speech Lang. Process. 28: 1678-1691 (2020) - [j6]Yiming Wu, Tristan Carsault, Eita Nakamura, Kazuyoshi Yoshii:
Semi-Supervised Neural Chord Estimation Based on a Variational Autoencoder With Latent Chord Labels and Features. IEEE ACM Trans. Audio Speech Lang. Process. 28: 2956-2966 (2020) - [c32]Ryoto Ishizuka, Ryo Nishikimi, Eita Nakamura, Kazuyoshi Yoshii:
Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training. APSIPA 2020: 359-364 - [c31]Yiming Wu, Eita Nakamura, Kazuyoshi Yoshii:
A Variational Autoencoder for Joint Chord and Key Estimation from Audio Chromagrams. APSIPA 2020: 500-506 - [c30]Yasuyuki Saito, Yasuji Sakai, Yuu Igarashi, Suguru Agata, Eita Nakamura, Shigeki Sagayama:
Music Recreation in Nursing Home using Automatic Music Accompaniment System and Score of VLN. LifeTech 2020: 127-131 - [i13]Yiming Wu, Tristan Carsault, Eita Nakamura, Kazuyoshi Yoshii:
Semi-supervised Neural Chord Estimation Based on a Variational Autoencoder with Discrete Labels and Continuous Textures of Chords. CoRR abs/2005.07091 (2020) - [i12]Kentaro Shibata, Eita Nakamura, Kazuyoshi Yoshii:
Non-Local Musical Statistics as Guides for Audio-to-Score Piano Transcription. CoRR abs/2008.12710 (2020) - [i11]Ryoto Ishizuka, Ryo Nishikimi, Eita Nakamura, Kazuyoshi Yoshii:
Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training. CoRR abs/2010.03749 (2020)
2010 – 2019
- 2019
- [c29]Yui Uehara, Eita Nakamura, Satoshi Tojo:
Chord Function Identification with Modulation Detection Based on HMM. CMMR 2019: 166-178 - [c28]Ryo Nishikimi, Eita Nakamura, Satoru Fukayama, Masataka Goto, Kazuyoshi Yoshii:
Automatic Singing Transcription Based on Encoder-decoder Recurrent Neural Networks with a Weakly-supervised Attention Mechanism. ICASSP 2019: 161-165 - [c27]Andrew McLeod, Eita Nakamura, Kazuyoshi Yoshii:
Improved Metrical Alignment of Midi Performance Based on a Repetition-aware Online-adapted Grammar. ICASSP 2019: 186-190 - [c26]Eita Nakamura, Kentaro Shibata, Ryo Nishikimi, Kazuyoshi Yoshii:
Unsupervised Melody Style Conversion. ICASSP 2019: 196-200 - [c25]Kentaro Shibata, Ryo Nishikimi, Satoru Fukayama, Masataka Goto, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Joint Transcription of Lead, Bass, and Rhythm Guitars Based on a Factorial Hidden Semi-Markov Model. ICASSP 2019: 236-240 - [c24]Shun Ueda, Kentaro Shibata, Yusuke Wada, Ryo Nishikimi, Eita Nakamura, Kazuyoshi Yoshii:
Bayesian Drum Transcription Based on Nonnegative Matrix Factor Decomposition with a Deep Score Prior. ICASSP 2019: 456-460 - [c23]Go Shibata, Ryo Nishikimi, Eita Nakamura, Kazuyoshi Yoshii:
Statistical Music Structure Analysis Based on a Homogeneity-, Repetitiveness-, and Regularity-Aware Hierarchical Hidden Semi-Markov Model. ISMIR 2019: 268-275 - [c22]Tristan Carsault, Andrew McLeod, Philippe Esling, Jérôme Nika, Eita Nakamura, Kazuyoshi Yoshii:
Multi-Step Chord Sequence Prediction Based On Aggregated Multi-Scale Encoder-Decoder Networks. MLSP 2019: 1-6 - [c21]Ryo Nishikimi, Eita Nakamura, Masataka Goto, Kazuyoshi Yoshii:
End-To-End Melody Note Transcription Based on a Beat-Synchronous Attention Mechanism. WASPAA 2019: 26-30 - [i10]Eita Nakamura, Yasuyuki Saito, Kazuyoshi Yoshii:
Statistical Learning and Estimation of Piano Fingering. CoRR abs/1904.10237 (2019) - [i9]Eita Nakamura, Kazuyoshi Yoshii:
Music Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions. CoRR abs/1908.06969 (2019) - [i8]Tristan Carsault, Andrew McLeod, Philippe Esling, Jérôme Nika, Eita Nakamura, Kazuyoshi Yoshii:
Multi-Step Chord Sequence Prediction Based on Aggregated Multi-Scale Encoder-Decoder Network. CoRR abs/1911.04972 (2019) - 2018
- [j5]Kousuke Itakura, Yoshiaki Bando, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii, Tatsuya Kawahara:
Bayesian Multichannel Audio Source Separation Based on Integrated Source and Spatial Models. IEEE ACM Trans. Audio Speech Lang. Process. 26(4): 831-846 (2018) - [c20]Yusuke Wada, Ryo Nishikimi, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Sequential Generation of Singing F0 Contours from Musical Note Sequences Based on WaveNet. APSIPA 2018: 983-989 - [c19]Eita Nakamura, Ryo Nishikimi, Simon Dixon, Kazuyoshi Yoshii:
Probabilistic Sequential Patterns for Singing Transcription. APSIPA 2018: 1905-1912 - [c18]Kazuyoshi Yoshii, Koichi Kitamura, Yoshiaki Bando, Eita Nakamura, Tatsuya Kawahara:
Independent Low-Rank Tensor Analysis for Audio Source Separation. EUSIPCO 2018: 1657-1661 - [c17]Eita Nakamura, Emmanouil Benetos, Kazuyoshi Yoshii, Simon Dixon:
Towards Complete Polyphonic Music Transcription: Integrating Multi-Pitch Detection and Rhythm Quantization. ICASSP 2018: 101-105 - [c16]Hiroaki Tsushima, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Interactive Arrangement of Chords and Melodies Based on a Tree-Structured Generative Model. ISMIR 2018: 145-151 - [i7]Eita Nakamura, Kazuyoshi Yoshii:
Statistical Piano Reduction Controlling Performance Difficulty. CoRR abs/1808.05006 (2018) - 2017
- [j4]Misato Ohkita, Yoshiaki Bando, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Audio-Visual Beat Tracking Based on a State-Space Model for a Robot Dancer Performing with a Human Dancer. J. Robotics Mechatronics 29(1): 125-136 (2017) - [j3]Eita Nakamura, Kazuyoshi Yoshii, Shigeki Sagayama:
Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices. IEEE ACM Trans. Audio Speech Lang. Process. 25(4): 794-806 (2017) - [j2]Eita Nakamura, Kazuyoshi Yoshii, Simon Dixon:
Note Value Recognition for Piano Transcription Using Markov Random Fields. IEEE ACM Trans. Audio Speech Lang. Process. 25(9): 1846-1858 (2017) - [c15]Kousuke Itakura, Yoshiaki Bando, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii, Tatsuya Kawahara:
Bayesian multichannel nonnegative matrix factorization for audio source separation and localization. ICASSP 2017: 551-555 - [c14]Eita Nakamura, Kazuyoshi Yoshii, Haruhiro Katayose:
Performance Error Detection and Post-Processing for Fast and Accurate Symbolic Music Alignment. ISMIR 2017: 347-353 - [c13]Ryo Nishikimi, Eita Nakamura, Masataka Goto, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Scale- and Rhythm-Aware Musical Note Estimation for Vocal F0 Trajectories Based on a Semi-Tatum-Synchronous Hierarchical Hidden Semi-Markov Model. ISMIR 2017: 376-382 - [c12]Hiroaki Tsushima, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Function- and Rhythm-Aware Melody Harmonization Based on Tree-Structured Parsing and Split-Merge Sampling of Chord Sequences. ISMIR 2017: 502-508 - [c11]Kazuyoshi Yoshii, Eita Nakamura, Katsutoshi Itoyama, Masataka Goto:
Infinite probabilistic latent component analysis for audio source separation. MLSP 2017: 1-6 - [i6]Eita Nakamura, Kazuyoshi Yoshii, Shigeki Sagayama:
Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices. CoRR abs/1701.08343 (2017) - [i5]Eita Nakamura, Kazuyoshi Yoshii, Simon Dixon:
Note Value Recognition for Rhythm Transcription Using a Markov Random Field Model for Musical Scores and Performances of Piano Music. CoRR abs/1703.08144 (2017) - [i4]Hiroaki Tsushima, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Generative Statistical Models with Self-Emergent Grammar of Chord Sequences. CoRR abs/1708.02255 (2017) - 2016
- [j1]Tomohiko Nakamura, Eita Nakamura, Shigeki Sagayama:
Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips. IEEE ACM Trans. Audio Speech Lang. Process. 24(2): 329-339 (2016) - [c10]Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Rhythm transcription of MIDI performances based on hierarchical Bayesian modelling of repetition and modification of musical note patterns. EUSIPCO 2016: 1946-1950 - [c9]Kousuke Itakura, Yoshiaki Bando, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
A unified Bayesian model of time-frequency clustering and low-rank approximation for multi-channel source separation. EUSIPCO 2016: 2280-2284 - [c8]Eita Nakamura, Masatoshi Hamanaka, Keiji Hirata, Kazuyoshi Yoshii:
Tree-structured probabilistic model of monophonic written music based on the generative theory of tonal music. ICASSP 2016: 276-280 - [c7]Yuta Ojima, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
A Hierarchical Bayesian Model of Chords, Pitches, and Spectrograms for Multipitch Analysis. ISMIR 2016: 309-315 - [c6]Ryo Nishikimi, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii:
Musical Note Estimation for F0 Trajectories of Singing Voices Based on a Bayesian Semi-Beat-Synchronous HMM. ISMIR 2016: 461-467 - 2015
- [c5]Eita Nakamura, Shigeki Sagayama:
Automatic Piano Reduction from Ensemble Scores Based on Merged-Output Hidden Markov Model. ICMC 2015 - [c4]Eita Nakamura, Philippe Cuvillier, Arshia Cont, Nobutaka Ono, Shigeki Sagayama:
Autoregressive Hidden Semi-Markov Model of Symbolic Music Performance for Score Following. ISMIR 2015: 392-398 - [c3]Eita Nakamura, Shinji Takaki:
Characteristics of Polyphonic Music Style and Markov Model of Pitch-Class Intervals. MCM 2015: 109-114 - [i3]Tomohiko Nakamura, Eita Nakamura, Shigeki Sagayama:
Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips. CoRR abs/1512.07748 (2015) - 2014
- [c2]Eita Nakamura, Nobutaka Ono, Yasuyuki Saito, Shigeki Sagayama:
Merged-Output Hidden Markov Model for Score Following of MIDI Performance with Ornaments, Desynchronized Voices, Repeats and Skips. ICMC 2014 - [c1]Eita Nakamura, Nobutaka Ono, Shigeki Sagayama:
Merged-Output HMM for Piano Fingering of Both Hands. ISMIR 2014: 531-536 - [i2]Eita Nakamura, Tomohiko Nakamura, Yasuyuki Saito, Nobutaka Ono, Shigeki Sagayama:
Outer-Product Hidden Markov Model and Polyphonic MIDI Score Following. CoRR abs/1404.2313 (2014) - [i1]Eita Nakamura, Nobutaka Ono, Shigeki Sagayama, Kenji Watanabe:
A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments. CoRR abs/1404.2314 (2014)
Coauthor Index
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