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We propose a unified model for three inter-related tasks: 1) to\textit{separate} individual sound sources from a mixed music audio, 2) to\textit{transcribe} each sound source to MIDI notes, and 3) to\textit{synthesize} new pieces based on the timbre of separated sources. The model isinspired by the fact that when humans listen to music, our minds can not onlyseparate the sounds of different instruments, but also at the same timeperceive high-level representations such as score and timbre. To mirror suchcapability computationally, we designed a pitch-timbre disentanglement modulebased on a popular encoder-decoder neural architecture for source separation.The key inductive biases are vector-quantization for pitch representation andpitch-transformation invariant for timbre representation. In addition, weadopted a query-by-example method to achieve \textit{zero-shot} learning, i.e.,the model is capable of doing source separation, transcription, and synthesisfor \textit{unseen} instruments. The current design focuses on audio mixturesof two monophonic instruments. Experimental results show that our modeloutperforms existing multi-task baselines, and the transcribed score serves asa powerful auxiliary for separation tasks.

A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis