Lightweight networks trained only on autoencoder latent codes can do bandwidth extension and mono-to-stereo upmixing at a fraction of the FLOPS of raw-audio models, but match those models only when the baselines are also degraded by the same autoencoder.
Audioldm: Text-to-audio generation with latent diffusion models,
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Learning to Upsample and Upmix Audio in the Latent Domain
Lightweight networks trained only on autoencoder latent codes can do bandwidth extension and mono-to-stereo upmixing at a fraction of the FLOPS of raw-audio models, but match those models only when the baselines are also degraded by the same autoencoder.