Music Generation
117 papers with code • 0 benchmarks • 23 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
JSB Chorales
Music21
URMP
Lakh MIDI Dataset
POP909
MuseData
EMOPIA
Most implemented papers
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model.
This Time with Feeling: Learning Expressive Musical Performance
Natooz/MidiTok
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Music generation has generally been focused on either creating scores or interpreting them.
MelNet: A Generative Model for Audio in the Frequency Domain
fatchord/MelNet
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Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps.
It's Raw! Audio Generation with State-Space Models
SaShiMi yields state-of-the-art performance for unconditional waveform generation in the autoregressive setting.
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
We conduct a user study to compare the melody of eight-bar long generated by MidiNet and by Google's MelodyRNN models, each time using the same priming melody.
Counterpoint by Convolution
czhuang/coconet
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Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end.
Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs
YatingMusic/compound-word-transformer
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In this paper, we present a conceptually different approach that explicitly takes into account the type of the tokens, such as note types and metric types.
A Critical Review of Recurrent Neural Networks for Sequence Learning
Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes.
Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation
Experimental results show that using binary neurons instead of HT or BS indeed leads to better results in a number of objective measures.
MMM : Exploring Conditional Multi-Track Music Generation with the Transformer
Natooz/MidiTok
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We propose the Multi-Track Music Machine (MMM), a generative system based on the Transformer architecture that is capable of generating multi-track music.
