The present disclosure describes techniques for controllable music generation. The techniques comprise extracting latent vectors from unlabelled data, the unlabelled data comprising a plurality of music note sequences, the plurality of music note sequences indicating a plurality of pieces of music; clustering the latent vectors into a plurality of classes corresponding to a plurality of music styles; generating a plurality of labelled latent vectors corresponding to the plurality of music styles, each of the plurality labelled latent vectors comprising information indicating features of a corresponding music style; and generating a first music note sequence indicating a first piece of music in a particular music style among the plurality of music styles based at least in part on a particular labelled latent vector among the plurality of labelled latent vectors, the particular labelled latent vector corresponding to the particular music style.

The present invention provides a system for a customized AI composition platform and an operating method of a music curator device. The system comprises a music curator device training deep learning models to generate songs and a platform server providing comprehensive services by providing learning data and deep learning models for automatic composition and collecting trained deep learning models and/or generated song data from at least one or more music curator device, wherein the music curator device transmits a deep learning model trained by a music curator or song data generated by the trained deep learning model to an open market platform.

In implementations of music enhancement systems, a computing device implements an enhancement system to receive input data describing a recorded acoustic waveform of a musical instrument. The recorded acoustic waveform is represented as an input mel spectrogram. The enhancement system generates an enhanced mel spectrogram by processing the input mel spectrogram using a first machine learning model trained on a first type of training data to generate enhanced mel spectrograms based on input mel spectrograms. An acoustic waveform of the musical instrument is generated by processing the enhanced mel spectrogram using a second machine learning model trained on a second type of training data to generate acoustic waveforms based on mel spectrograms. The acoustic waveform of the musical instrument does not include an acoustic artifact that is included in the recorded waveform of the musical instrument.