https://github.com/tayjsl97/muser
공식 코드MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion
Papers with Code 아카이브
Music Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Musique guitar
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tencent-ailab/songgeneration
공식 코드LeVo: High-Quality Song Generation with Multi-Preference Alignment
https://github.com/tiffanyblews/mozartstouch
공식 코드https://github.com/wbs2788/vmb
공식 코드Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation
https://github.com/wzk1015/awesome-vision-to-music-generation
공식 코드https://github.com/xzwy/msldm
공식 코드https://github.com/yjhuangcd/rule-guided-music
공식 코드Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
https://github.com/yuer867/emo-disentanger
공식 코드Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation
https://github.com/zacharynovack/lead-ae
공식 코드https://github.com/zelaki/DreamSound
공식 코드Investigating Personalization Methods in Text to Music Generation
https://github.com/zeyuet/vidmuse
공식 코드VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling
https://github.com/zhaojw1998/accomontage
공식 코드AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer
https://github.com/zhaojw1998/accomontage-3
공식 코드Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling
https://github.com/zhuole1025/symmv
공식 코드Video Background Music Generation: Dataset, Method and Evaluation
https://github.com/zjy0401/cocoformer
공식 코드CoCoFormer: A controllable feature-rich polyphonic music generation method
https://github.com/zzwaang/melody-reduction-algo
공식 코드Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models
https://github.com/zzwaang/whole-song-gen
공식 코드Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models
https://gitlab.com/sanidhyamangal/music_research
공식 코드https://gitlab.com/skalo/baroque-nmt
공식 코드Modeling Baroque Two-Part Counterpoint with Neural Machine Translation
https://huggingface.co/JoshuaW1997/FUTGA
공식 코드Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation
https://github.com/0417keito/JEN-1-COMPOSER-pytorch
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
https://github.com/0417keito/JEN-1-pytorch
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
https://github.com/AI-Guru/MMM-JSB
MMM : Exploring Conditional Multi-Track Music Generation with the Transformer
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/Chatha-Sphere/pno-ai
This Time with Feeling: Learning Expressive Musical Performance
https://github.com/ElectricAlexis/NotaGen
https://github.com/GiadaSimionato/Analysis_and_Comparison_of_Deep_Learning_Methods_for_Jazz_Music_Generation
https://github.com/IAHispano/Applio/tree/exp/vocoders/rvc/lib/algorithm
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
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/KarenMars/TUD_MMSR
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
https://github.com/MindSpore-scientific-2/code-11/tree/main/text-to-music
Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion
https://github.com/MindSpore-scientific-2/code-2/tree/main/text-to-music
Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion
https://github.com/MindSpore-scientific/code-1/tree/main/token_learner
Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task
https://github.com/MindSpore-scientific/code-10/tree/main/text-to-music
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.