Abstract
The application of deep neural networks, particularly convolutional neural networks, in the field of music auto-tagging has been gaining traction in recent times. These deep networks relieve the engineers from the burden of handcrafting domain-specific features. However, musical features often show great temporal diversity which traditional deep networks are unable to capture. Keeping this in mind, we propose a convolutional neural network architecture which attempts to learn features over multiple timescales. The architecture runs multiple convolutions over various subsampled versions of the original audio spectrogram. These convolution streams are then concatenated to make the tag predictions. We evaluate the architecture on the MagnaTagATune dataset, and we show that the proposed architecture yields results close to the state of the art and comprehensively beats shallow classifiers trained on handcrafted features.
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Dabral, T.S., Deshmukh, A.S., Malapati, A. (2019). A Multi-scale Convolutional Neural Network Architecture for Music Auto-Tagging. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_60
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DOI: https://doi.org/10.1007/978-981-13-1592-3_60
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