Music Generation
129 papers with code • 0 benchmarks • 24 datasets
Music Generation is the task of generating music or music-like sounds from a model or algorithm. The goal is to produce a sequence of notes or sound events that are similar to existing music in some way, such as having the same style, genre, or mood.
Benchmarks
These leaderboards are used to track progress in Music Generation
Libraries
Use these libraries to find Music Generation models and implementationsDatasets
NSynth
MAESTRO
MusicCaps
JSB Chorales
URMP
Music21
POP909
MuseData
EMOPIA
Latest papers
Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model
These distinct features are then employed as guiding input to our music generation model.
JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation
0417keito/JEN-1-COMPOSER-pytorch
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With rapid advances in generative artificial intelligence, the text-to-music synthesis task has emerged as a promising direction for music generation from scratch.
miditok: A Python package for MIDI file tokenization
Natooz/MidiTok
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Recent progress in natural language processing has been adapted to the symbolic music modality.
Content-based Controls For Music Large Language Modeling
We aim to further equip the models with direct and content-based controls on innate music languages such as pitch, chords and drum track.
Unsupervised Lead Sheet Generation via Semantic Compression
Lead sheets have become commonplace in generative music research, being used as an initial compressed representation for downstream tasks like multitrack music generation and automatic arrangement.
CoCoFormer: A controllable feature-rich polyphonic music generation method
This paper explores the modeling method of polyphonic music sequence.
Impact of time and note duration tokenizations on deep learning symbolic music modeling
Natooz/music-modeling-time-duration
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Symbolic music is widely used in various deep learning tasks, including generation, transcription, synthesis, and Music Information Retrieval (MIR).
Investigating Personalization Methods in Text to Music Generation
In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting.
Exploring XAI for the Arts: Explaining Latent Space in Generative Music
bbanar2/exploring_xai_in_genmus_via_lsr
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We increase the explainability of the model by: i) using latent space regularisation to force some specific dimensions of the latent space to map to meaningful musical attributes, ii) providing a user interface feedback loop to allow people to adjust dimensions of the latent space and observe the results of these changes in real-time, iii) providing a visualisation of the musical attributes in the latent space to help people understand and predict the effect of changes to latent space dimensions.
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization.
