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Description: A spectral approach to audio analysis and generation with neural networks (LSTM). The techniques included here were used as part of the Mezzanine Vs. MAGNet project featured as part of the Barbican's AI: More than Human exhibition It represents ongoing work from researchers at The Creative Computing Institute, UAL and Goldsmiths, University of London. MAGNet trains on the magnitude spectra of acoustic audio signals, and reproduces entirely new magnitude spectra that can be turned back in to sound using phase reconstruction - it's very high quality in terms of audio fidelity. This repo provides a chance for people to train their own models with their own source audio and genreate new sounds. Both given projects are designed to be simple to understand and easy to run.
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Input types: Audio MIDI Text None Genre Metadata Image
Output types: Audio MIDI
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Technology: Not Specified Latent Consistency Model Latent Diffusion LSTM VAE Sequence-to-sequence neural network Transformer Suite of AI tools Diffusion Hierarchical Recurrent Neural Network (RNN) Autoregressive Convolutional Neural Network
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Has real time inference: Yes No Not known
Is free: Yes No Yes and No, depending on the plan Not known
Is open source: Yes No Not known
Are checkpoints available: Yes No Not known
Can finetune: Yes No Not known
Can train from scratch: Yes No Not known
Tags: text-to-audio MIDI text-prompt small-dataset open-source low-resource free checkpoints proprietary no-input image-to-audio API
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