Music Generation
134 papers with code • 0 benchmarks • 25 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
Music21
URMP
POP909
MuseData
EMOPIA
Latest papers
Mozart's Touch: A Lightweight Multi-modal Music Generation Framework Based on Pre-Trained Large Models
In recent years, AI-Generated Content (AIGC) has witnessed rapid advancements, facilitating the generation of music, images, and other forms of artistic expression across various industries.
ComposerX: Multi-Agent Symbolic Music Composition with LLMs
Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints.
COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
gladia-research-group/cocola
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We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples.
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges
This survey also highlights diverse applications of SSMs across domains such as vision, video, audio, speech, language (especially long sequence modeling), medical (including genomics), chemical (like drug design), recommendation systems, and time series analysis, including tabular data.
Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey
dinhviettoanle/survey-music-nlp
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Music has been frequently compared to language, as they share several similarities, including sequential representations of text and music.
Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
yjhuangcd/rule-guided-music
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We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time.
Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls
kikyo-16/airgen
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We apply this method to fine-tune MusicGen, a leading autoregressive music generation model.
PAM: Prompting Audio-Language Models for Audio Quality Assessment
soham97/pam
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Here, we exploit this capability and introduce PAM, a no-reference metric for assessing audio quality for different audio processing tasks.
Combinatorial music generation model with song structure graph analysis
slslslrhfem/song_structure_graph
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In this work, we propose a symbolic music generation model with the song structure graph analysis network.
MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion
tayjsl97/muser
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However, prior research on deep learning-based emotional music generation has rarely explored the contribution of different musical elements to emotions, let alone the deliberate manipulation of these elements to alter the emotion of music, which is not conducive to fine-grained element-level control over emotions.
