Papers with Code 아카이브
Music Modeling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 18
<span style="color:grey; opacity: 0.6">( Image credit: R-Transformer )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Andrea-V/MusAE
공식 코드Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders
https://github.com/DSE-MSU/R-transformer
공식 코드R-Transformer: Recurrent Neural Network Enhanced Transformer
https://github.com/LUMII-Syslab/RRU
공식 코드https://github.com/Natooz/music-modeling-time-duration
공식 코드Impact of time and note duration tokenizations on deep learning symbolic music modeling
https://github.com/YatingMusic/remi
공식 코드Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions
https://github.com/czhuang/coconet
공식 코드https://github.com/f90/Seq-U-Net
공식 코드Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling
https://github.com/gtshs2/BetaLSTM
공식 코드https://github.com/guozixunnicolas/fundamentalmusicembedding
공식 코드https://github.com/jryban/frechet-music-distance
공식 코드Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation
https://github.com/justinchiu/low-rank-models
공식 코드Low-Rank Constraints for Fast Inference in Structured Models
https://github.com/jych/librnn
공식 코드Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
https://github.com/locuslab/TCN
공식 코드An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/marcofraccaro/srnn
공식 코드https://github.com/mkhodak/relax
공식 코드https://github.com/nick11roberts/XD
공식 코드https://github.com/omarperacha/TonicNet
공식 코드Improving Polyphonic Music Models with Feature-Rich Encoding
https://github.com/ycemsubakan/diagonal_rnns
공식 코드https://github.com/2023-MindSpore-1/ms-code-14/tree/main/LSTM
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/atae_lstm
https://github.com/AI-Guru/tonicnet
Improving Polyphonic Music Models with Feature-Rich Encoding
https://github.com/BShakhovsky/PolyphonicPianoTranscription
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
https://github.com/Baichenjia/Tensorflow-TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/Cassettes/Research
Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions
https://github.com/Chatha-Sphere/pno-ai
https://github.com/GiadaSimionato/Analysis_and_Comparison_of_Deep_Learning_Methods_for_Jazz_Music_Generation
https://github.com/Jesplar/LSTM-MusicGenerator
https://github.com/Kangmo/kogenta
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
https://github.com/MChen9/TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/MindSpore-scientific/code-13/tree/main/text-to-music
https://github.com/Natooz/MidiTok
https://github.com/Nic5472K/FriendsOOGroup_TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/RohanAsnani/capstone_project_machine_learning-nanodegree
https://github.com/ShotDownDiane/tcn-master
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/Songweiping/TCN-TF
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.