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arXiv · 2210.00434

Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings

Zhihuan Kuang·Shi Zong·Jianbing Zhang·Jiajun Chen·Hongfu Liu
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Abstract

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.

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Zhihuan Kuang, Shi Zong, Jianbing Zhang, Jiajun Chen, Hongfu Liu. 2022-10-02. Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings. https://arxiv.org/abs/2210.00434

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