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

• URL: http://arxiv.org/abs/2210.00434v2
• Date: Mon, 8 May 2023 03:09:27 GMT
• Title: Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings
• Authors: Zhihuan Kuang, Shi Zong, Jianbing Zhang, Jiajun Chen, Hongfu Liu
• Abstract summary: Music-to-text synaesthesia aims to generate descriptive texts from music recordings with the same sentiment for further understanding. 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.
• Score: 36.090928638883454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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