¿El Caballo Viejo? Latin Genre Recognition with Deep Learning and Spectral Periodicity

Sturm, Bob L.; Kereliuk, Corey; Larsen, Jan

doi:10.1007/978-3-319-20603-5_34

Cited by 2 publications

(2 citation statements)

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“…We find that through such a transformation we can make the system perform perfectly or no better than random by applying tempo changes of at most 6% to test dataset recordings. We find a similar result for the same kind of deep learning system but trained in LMD [52].…”

Section: Discussionsupporting

confidence: 72%

“…Our application of adversaries here is close to the "method of irrelevant transformations" that we apply in [48], [52], [53] to assess the internal models of music content analysis systems, and to test the hypothesis, "the system is using relevant criteria to make its decisions." In [48], we take a brute force approach whereby we apply random but linear time-invariant and minor filtering to inputs of systems trained in three different music recording datasets until their FoM becomes perfect or random.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning and Music Adversaries

Kereliuk

Sturm

Larsen

2015

IEEE Trans. Multimedia

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110

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Abstract-An adversary is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative. Recent work builds adversaries for deep learning systems applied to image object recognition, which exploits the parameters of the system to find the minimal perturbation of the input image such that the network misclassifies it with high confidence. We adapt this approach to construct and deploy an adversary of deep learning systems applied to music content analysis. In our case, however, the input to the systems is magnitude spectral frames, which requires special care in order to produce valid input audio signals from network-derived perturbations. For two different train-test partitionings of two benchmark datasets, and two different deep architectures, we find that this adversary is very effective in defeating the resulting systems. We find the convolutional networks are more robust, however, compared with systems based on a majority vote over individually classified audio frames. Furthermore, we integrate the adversary into the training of new deep systems, but do not find that this improves their resilience against the same adversary.

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Section: Discussionsupporting

confidence: 72%