Papers with Code 아카이브
Music Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 1
- 보존된 평가 기록
- 1
Musique guitar
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bonheml/vae_learning_dynamics
공식 코드https://github.com/brown-palm/syntheory
공식 코드https://github.com/carlosholivan/DeepLearningMusicGeneration
공식 코드https://github.com/carlosholivan/symbolic-music-structure-analysis
공식 코드Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods
https://github.com/chrisdonahue/LakhNES
공식 코드LakhNES: Improving multi-instrumental music generation with cross-domain pre-training
https://github.com/chrisdonahue/nesmdb
공식 코드The NES Music Database: A multi-instrumental dataset with expressive performance attributes
https://github.com/christianazinn/midi-rwkv
공식 코드Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV
https://github.com/crypto-code/mu-llama
공식 코드Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning
https://github.com/cypress-yang/songbloom
공식 코드https://github.com/czhuang/coconet
공식 코드https://github.com/dada-bots/dadaGP
공식 코드DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
https://github.com/deezer/robust-ai-lyrics-detection
공식 코드Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion
https://github.com/dinhviettoanle/survey-music-nlp
공식 코드https://github.com/dvruette/figaro
공식 코드FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control
https://github.com/emanuelecosenza/polyphemus
공식 코드https://github.com/emilianpostolache/stable-audio-controlnet
공식 코드COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
https://github.com/facebookresearch/SING
공식 코드https://github.com/feizc/fluxmusic
공식 코드https://github.com/felipemarra/babel-bardo
공식 코드https://github.com/funaudiollm/inspiremusic
공식 코드https://github.com/gablg1/ORGAN
공식 코드Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
https://github.com/gariscat/loopy
공식 코드LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music
https://github.com/gcunhase/Emotional-Video-to-Audio-with-ANFIS-DeepRNN
공식 코드https://github.com/gladia-research-group/cocola
공식 코드COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
https://github.com/gladia-research-group/loopgen
공식 코드https://github.com/gladia-research-group/multi-source-diffusion-models
공식 코드Multi-Source Diffusion Models for Simultaneous Music Generation and Separation
https://github.com/gudgud96/music-fader-nets
공식 코드https://github.com/guozixunnicolas/fundamentalmusicembedding
공식 코드https://github.com/harvardnlp/TextFlow
공식 코드https://github.com/hazyresearch/state-spaces
공식 코드https://github.com/heqi201255/tomi
공식 코드TOMI: Transforming and Organizing Music Ideas for Multi-Track Compositions with Full-Song Structure
https://github.com/hugofloresgarcia/vampnet
공식 코드VampNet: Music Generation via Masked Acoustic Token Modeling
https://github.com/ivcylc/openmusic
공식 코드Quality-aware Masked Diffusion Transformer for Enhanced Music Generation
https://github.com/ivcylc/qa-mdt
공식 코드Quality-aware Masked Diffusion Transformer for Enhanced Music Generation
https://github.com/jacksonkaunismaa/neural-music
공식 코드cMelGAN: An Efficient Conditional Generative Model Based on Mel Spectrograms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.