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End-to-end learning for music audio tagging at scale | | Jordi Pons
; Oriol Nieto
; Matthew Prockup
; Erik M. Schmidt
; Andreas F. Ehmann
; Xavier Serra
; | | Date: |
7 Nov 2017 | | Abstract: | The lack of data tends to limit the outcomes of deep learning research -
specially, when dealing with end-to-end learning stacks processing raw data
such as waveforms. In this study we make use of musical labels annotated for
1.2 million tracks. This large amount of data allows us to unrestrictedly
explore different front-end paradigms: from assumption-free models - using
waveforms as input with very small convolutional filters; to models that rely
on domain knowledge - log-mel spectrograms with a convolutional neural network
designed to learn temporal and timbral features. Results suggest that while
spectrogram-based models surpass their waveform-based counterparts, the
difference in performance shrinks as more data are employed. | | Source: | arXiv, 1711.2520 | | Services: | Forum | Review | PDF | Favorites | |
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