Shuvaev, Sergey, Giaffar, Hamza, Koulakov, Alexei A (December 2017) Representations of Sound in Deep Learning of Audio Features from Music. arXiv. (Submitted)
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Abstract
The work of a single musician, group or composer can vary widely in terms of musical style. Indeed, different stylistic elements, from performance medium and rhythm to harmony and texture, are typically exploited and developed across an artist's lifetime. Yet, there is often a discernable character to the work of, for instance, individual composers at the perceptual level - an experienced listener can often pick up on subtle clues in the music to identify the composer or performer. Here we suggest that a convolutional network may learn these subtle clues or features given an appropriate representation of the music. In this paper, we apply a deep convolutional neural network to a large audio dataset and empirically evaluate its performance on audio classification tasks. Our trained network demonstrates accurate performance on such classification tasks when presented with 5 s examples of music obtained by simple transformations of the raw audio waveform. A particularly interesting example is the spectral representation of music obtained by application of a logarithmically spaced filter bank, mirroring the early stages of auditory signal transduction in mammals. The most successful representation of music to facilitate discrimination was obtained via a random matrix transform (RMT). Networks based on logarithmic filter banks and RMT were able to correctly guess the one composer out of 31 possibilities in 68 and 84 percent of cases respectively.
| Item Type: | Paper |
|---|---|
| Subjects: | organism description > animal > mammal organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks |
| CSHL Authors: | |
| Communities: | CSHL labs > Koulakov lab School of Biological Sciences > Publications |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 7 December 2017 |
| Date Deposited: | 13 Oct 2023 14:17 |
| Last Modified: | 29 Feb 2024 19:53 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41226 |
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