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Dilated Convolutional Model for Melody Extraction

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posted on 03.02.2022, 04:51 authored by Xian WangXian Wang, Lingqiao LiuLingqiao Liu, Javen ShiJaven Shi
Melody extraction is a challenging task in music information retrieval that enables many down-stream applications. In this paper we propose a simple dilated convolutional model for melody extraction. It takes variable-q transforms as inputs. It first uses consecutive layers of convolution to capture local temporal-frequency patterns. Afterward, it relies only a single layer of dilated convolution for capturing global frequency patterns formed by the pitches and harmonics of active notes. This model is effective in that it achieves the-state-of-the-art performance on most datasets, for both general and vocal melody extraction. In addition, it gets the best performance with the least training data.

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Email Address of Submitting Author

xian.wang01@adelaide.edu.au

ORCID of Submitting Author

0000-0003-2306-1503

Submitting Author's Institution

The University of Adelaide

Submitting Author's Country

Australia