  Could we automatically derive the score of a piano accompaniment based on the
audio of a pop song? This is the audio-to-symbolic arrangement problem we
tackle in this paper. A good arrangement model should not only consider the
audio content but also have prior knowledge of piano composition (so that the
generation "sounds like" the audio and meanwhile maintains musicality). To this
end, we contribute a cross-modal representation-learning model, which 1)
extracts chord and melodic information from the audio, and 2) learns texture
representation from both audio and a corrupted ground truth arrangement. We
further introduce a tailored training strategy that gradually shifts the source
of texture information from corrupted score to audio. In the end, the
score-based texture posterior is reduced to a standard normal distribution, and
only audio is needed for inference. Experiments show that our model captures
major audio information and outperforms baselines in generation quality.
