Rhythm generator using Variational Autoencoder (VAE). Based on M4L.RhythmVAE by Nao Tokui, modded and extended to support simple and compound meter rhythms, with minimal amount of training data. Similarly to RhythmVAE, the goal of R-VAE is the exploration of latent spaces of musical rhythms. Unlike most previous work in rhythm modeling, R-VAE can be trained with small datasets, enabling rapid customization and exploration by individual users. R-VAE employs a data representation that encodes simple and compound meter rhythms. Models and latent space visualizations for R-VAE are available on the project's GitHub page: https://github.com/vigliensoni/R-VAE-models.
Year: 2022
Website: https://github.com/vigliensoni/R-VAE
Input types: MIDI
Output types: MIDI
Output length: 2 bars
AI Technique: VAE
Dataset: "The Future Sample Pack"
License type: GPLv3
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RAVE is an audio processing/generativity based on deep learning. RAVE (Realtime Audio Variational autoEncoder) is a learning framework for generating a neural network model from audio data. RAVE allowing both fast and high-quality audio waveform synthesis (20x real-time at 48 kHz sampling rate on standard CPU). In Max and Pd, it is accompanied by its nn~ decoder, which enables these models to be used in real time for various applications, audio generativity/timbre transformation/transfer.
Year: 2022
Website: https://forum.ircam.fr/collections/detail/rave/
Input types: Audio
Output types: Audio
Output length: Variable / Audio buffer size
AI Technique: VAE
Dataset: N/A
License type: MIT
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WIP
Year: 2016
Website: https://github.com/soroushmehr/sampleRNN_ICLR2017
Input types: Audio
Output types: Audio
Output length: Variable
AI Technique: Hierarchical Recurrent Neural Network (RNN)
Dataset: Not disclosed
License type: MIT
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Mustango is an open-source Text-to-Music model with focus on fine controllability allowing to specify musical attributes such as key or chord sequences.
Year: 2023
Website: https://amaai-lab.github.io/mustango/
Input types: Text
Output types: Audio
Output length: 10 sec
AI Technique: Latent Diffusion
Dataset: MusicBench
License type: MIT/CC-BY-SA
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