  In this paper, we consider a novel research problem: music-to-text
synaesthesia. Different from the classical music tagging problem that
classifies a music recording into pre-defined categories, music-to-text
synaesthesia aims to generate descriptive texts from music recordings with the
same sentiment for further understanding. As existing music-related datasets do
not contain the semantic descriptions on music recordings, we collect a new
dataset that contains 1,955 aligned pairs of classical music recordings and
text descriptions. Based on this, we build a computational model to generate
sentences that can describe the content of the music recording. To tackle the
highly non-discriminative classical music, we design a group
topology-preservation loss, which considers more samples as a group reference
and preserves the relative topology among different samples. Extensive
experimental results qualitatively and quantitatively demonstrate the
effectiveness of our proposed model over five heuristics or pre-trained
competitive methods and their variants on our collected dataset.
