A hierarchical representation learning approach for source separation, transcription, and music generation

With interpretable music representation learning, music source separation problems are well connected with transcription problems, and transcription problems can be transformed into music arrangement problems. In particular, Gus will discuss two recently developed models. The first one used a pitch-timbre disentanglement to achieve source separation, transcription, and synthesis. The second one used cross-modal chord-texture disentanglement to solve audio-to-symbolic piano arrangement. In the end, Gus will show his vision of a unified hierarchical representation-learning framework that bridges music understanding and generation.