References
- 1Akhtar, N., and Mian, A. S. (2018). Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access, 6: 14410–14430. DOI: 10.1109/ACCESS.2018.2807385
- 2Bhalke, D. G., Rao, C. B. R., and Bormane, D. S. (2016). Automatic musical instrument classification using fractional Fourier transform based MFCC features and counter propagation neural network. Journal of Intelligent Information Systems, 46(3): 425–446. DOI: 10.1007/s10844-015-0360-9
- 3Carlini, N., and Wagner, D. A. (2018). Audio adversarial examples: Targeted attacks on speech-to-text. In Proc. of the IEEE Security and Privacy Workshops, pages 1–7. IEEE. DOI: 10.1109/SPW.2018.00009
- 4Deldjoo, Y., Noia, T. D., and Merra, F. A. (2021). A survey on adversarial recommender systems: From attack/defense strategies to generative adversarial networks. ACM Computing Surveys, 54(2): 1–38. DOI: 10.1145/3439729
- 5Du, T., Ji, S., Li, J., Gu, Q., Wang, T., and Beyah, R. (2020). SirenAttack: Generating adversarial audio for end-to-end acoustic systems. In Proc. of the 15th ACM Asia Conference on Computer and Communications Security, pages 357–369.
ACM . DOI: 10.1145/3320269.3384733 - 6Engel, J. H., Hantrakul, L., Gu, C., and Roberts, A. (2020). DDSP: Differentiable digital signal processing. In Proc. of the 8th International Conference on Learning Representations.
- 7Feldbauer, R., and Flexer, A. (2019). A comprehensive empirical comparison of hubness reduction in high-dimensional spaces. Knowledge and Information Systems, 59(1): 137–166. DOI: 10.1007/s10115-018-1205-y
- 8Feldbauer, R., Rattei, T., and Flexer, A. (2020). scikithubness: Hubness reduction and approximate neighbor search. Journal of Open Source Software, 5(45): 1957. DOI: 10.21105/joss.01957
- 9Flexer, A., Dörfler, M., Schlüter, J., and Grill, T. (2018). Hubness as a case of technical algorithmic bias in music recommendation. In Proc. of the IEEE International Conference on Data Mining Workshops, pages 1062–1069.
IEEE . DOI: 10.1109/ICDMW.2018.00154 - 10Flexer, A., and Stevens, J. (2018). Mutual proximity graphs for improved reachability in music recommendation. Journal of New Music Research, 47(1): 17–28. DOI: 10.1080/09298215.2017.1354891
- 11Fonseca, E., Plakal, M., Font, F., Ellis, D. P., and Serra, X. (2019).
Audio tagging with noisy labels and minimal supervision . In Proc. of the Detection and Classification of Acoustic Scenes and Events Workshop, pages 69–73. New York University. DOI: 10.33682/w13e-5v06 - 12Gasser, M., and Flexer, A. (2009). FM4 Soundpark: Audio-based music recommendation in everyday use. In Proc. of the 6th Sound and Music Computing Conference, pages 23–25.
- 13Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015). Explaining and harnessing adversarial examples. In Proc. of the 3rd International Conference on Learning Representations.
- 14Holzapfel, A., Sturm, B. L., and Coeckelbergh, M. (2018). Ethical dimensions of music information retrieval technology. Transactions of the International Society for Music Information Retrieval, 1(1): 44–55. DOI: 10.5334/tismir.13
- 15Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. of the 32nd International Conference on Machine Learning, pages 448–456.
- 16Kereliuk, C., Sturm, B. L., and Larsen, J. (2015). Deep learning, audio adversaries, and music content analysis. In Proc. of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pages 1–5.
IEEE . DOI: 10.1109/WASPAA.2015.7336950 - 17Kingma, D. P., and Ba, J. (2015). Adam: A method for stochastic optimization. In Proc. of the 3rd International Conference on Learning Representations.
- 18Knees, P., Schnitzer, D., and Flexer, A. (2014). Improving neighborhood-based collaborative filtering by reducing hubness. In Proc. of the 4th International Conference on Multimedia Retrieval, page 161.
ACM . DOI: 10.1145/2578726.2578747 - 19Lostanlen, V., Andén, J., and Lagrange, M. (2018). Extended playing techniques: The next milestone in musical instrument recognition. In Proc. of the 5th International Conference on Digital Libraries for Musicology, pages 1–10.
ACM . DOI: 10.1145/3273024.3273036 - 20Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. In Proc. of the 6th International Conference on Learning Representations.
- 21Mandel, M. I., and Ellis, D. P. (2005). Song-level features and support vector machines for music classification. In Proc. of the 6th International Conference on Music Information Retrieval, pages 594–599.
- 22Nair, V., and Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. In Proc. of the 27th International Conference on Machine Learning, pages 807–814.
- 23Pachet, F., and Aucouturier, J.-J. (2004). Improving timbre similarity: How high is the sky? Journal of Negative Results in Speech and Audio Sciences, 1(1): 1–13.
- 24Paischer, F., Prinz, K., and Widmer, G. (2019). Audio tagging with convolutional neural networks trained with noisy data. In Proc. of the Detection and Classification of Acoustic Scenes and Events Workshop.
- 25Qin, Y., Carlini, N., Cottrell, G. W., Goodfellow, I. J., and Raffel, C. (2019). Imperceptible, robust, and targeted adversarial examples for automatic speech recognition. In Proc. of the 36th International Conference on Machine Learning, pages 5231–5240.
- 26Radovanović, M., Nanopoulos, A., and Ivanović, M. (2010). Hubs in space: Popular nearest neighbors in high-dimensional data. Journal of Machine Learning Research, 11(86): 2487–2531.
- 27Rodríguez-Algarra, F., Sturm, B. L., and Dixon, S. (2019). Characterising confounding effects in music classification experiments through interventions. Transactions of the International Society for Music Information Retrieval, 2(1): 52–66. DOI: 10.5334/tismir.24
- 28Schedl, M. (2019). Deep learning in music recommendation systems. Frontiers in Applied Mathematics and Statistics, 5: 44. DOI: 10.3389/fams.2019.00044
- 29Schnitzer, D., Flexer, A., Schedl, M., and Widmer, G. (2012). Local and global scaling reduce hubs in space. Journal of Machine Learning Research, 13(1): 2871–2902.
- 30Schönherr, L., Kohls, K., Zeiler, S., Holz, T., and Kolossa, D. (2019). Adversarial attacks against automatic speech recognition systems via psychoacoustic hiding. In Proc. of the 26th Annual Network and Distributed System Security Symposium. The Internet Society. DOI: 10.14722/ndss.2019.23288
- 31Sturm, B. L. (2013). Classification accuracy is not enough: On the evaluation of music genre recognition systems. Journal of Intelligent Information Systems, 41(3): 371–406. DOI: 10.1007/s10844-013-0250-y
- 32Sturm, B. L. (2014). A simple method to determine if a music information retrieval system is a “horse”. IEEE Transactions on Multimedia, 16(6): 1636–1644. DOI: 10.1109/TMM.2014.2330697
- 33Sturm, B. L. (2016). The “horse” inside: Seeking causes behind the behaviors of music content analysis systems. Computers in Entertainment, 14(2): 1–32. DOI: 10.1145/2967507
- 34Subramanian, V., Pankajakshan, A., Benetos, E., Xu, N., McDonald, S., and Sandler, M. (2020). A study on the transferability of adversarial attacks in sound event classification. In Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, pages 301–305.
IEEE . DOI: 10.1109/ICASSP40776.2020.9054445 - 35Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R. (2014). Intriguing properties of neural networks. In Proc. of the 2nd International Conference on Learning Representations.
- 36Trochim, W. M., and Donnelly, J. P. (2001). The Research Methods Knowledge Base. Atomic Dog Publishing, Cincinnati, 2nd edition.
- 37Urbano, J., Schedl, M., and Serra, X. (2013). Evaluation in music information retrieval. Journal of Intelligent Information Systems, 41(3): 345–369. DOI: 10.1007/s10844-013-0249-4
- 38Zhang, W. E., Sheng, Q. Z., Alhazmi, A. A., and Li, C. (2020). Adversarial attacks on deep-learning models in natural language processing: A survey. ACM Transactions on Intelligent Systems and Technology, 11(3): 24: 1–41. DOI: 10.1145/3374217
