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A Deep Bag-of-Features Model for Music Auto-Tagging
[article]
2016
arXiv
pre-print
Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto-tagging. In this paper, we present a two-stage learning model to effectively predict multiple labels from music audio. The first stage learns to project local spectral patterns
arXiv:1508.04999v3
fatcat:dc2zlksbgnhbxmodrtgpvtd7te