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Music Analysis for Automatic Music Composition: Source Separation and Music Transcription | Taiwan AILabs
AI needs a lot of music examples to learn to compose music. The quality and diversity of the music examples can be the key to the success of the AI. Typically, researchers begin with training an AI music composition model by learning from symbolic music data such as MIDI files. This is how we developed the AI Jazz bass player introduced in our last blog post. However, relying on the MIDI files as the major data source has a few clear limitations. First, not all the music out there has MIDI files that are publicly and widely available. This is especially the case for certain music genres such as Jazz, which features improvisation. Second, MIDI files are notoriously noisy [1]. A great effort is needed in preprocessing and cleansing the MIDI data before they can be used to train a machine learning model. Such a process may come with assumptions, simplifications, and imprecisions that limit the performance of the resulting AI model. Third, not all MIDI files contain performance