Skip to main content

Music Genre Classification Using Federated Learning

  • Conference paper
  • First Online:
Information Systems for Intelligent Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 324))

  • 360 Accesses

Abstract

Federated learning (FL) is a decentralized privacy-preserving machine learning technique that allows models to be trained using input from multiple clients without requiring each client to send all of their data to a central server. In audio, FL and other privacy-preserving approaches have received comparatively little attention. A federated approach is implemented to preserve the copyright claims in the music industry and for music corporations to ensure discretion while using their sensitive data for training purposes in large-scale collaborative machine learning projects. We use audio from the GTZAN dataset to study the use of FL for the music genre classification task in this paper using convolutional neural networks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhang, J., Li, M., Zeng, S., Xie, B., Zhao, D.: A survey on security and privacy threats to federated learning. In: 2021 International Conference on Networking and Network Applications (NaNA), pp. 319–326. IEEE Access, Urumchi City, China (2021)

    Google Scholar 

  2. Mothukuri, V., Parizi, R.M., Pouriyeh, S., Huang, Y., et al.: A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 115, 619–640 (2021)

    Google Scholar 

  3. Bae, H., Jung, J., Jang, D., Ha, H., et al.: Security and Privacy Issues in Deep Learning. arXiv: 1807.11655 (2018)

    Google Scholar 

  4. Savazzi, S., Nicoli, M., Rampa, V.: Federated learning with cooperating devices: a consensus approach for massive IoT networks. IEEE Internet Things J. 7(5), 4641–4654 (2020)

    Google Scholar 

  5. Sheller, M.J., Edwards, B., Reina, G.A., Martin, J., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10, 12598 (2020)

    Google Scholar 

  6. Xu, J., Glicksberg, B.S., Su, C., et al.: Federated learning for healthcare informatics. J. Healthc. Inform. Res. 5, 1–19 (2021)

    Google Scholar 

  7. Nguyen, A., Do, T., Tran, M., Nguyen, B.X., et al.: Deep Federated Learning for Autonomous Driving. arXiv: 2110.05754 (2021)

    Google Scholar 

  8. Xu, R., Baracaldo, N., Zhou, Y., et al.: HybridAlpha: an efficient approach for privacy-preserving federated learning. In: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security (AISec'19), pp. 13–23. Association for Computing Machinery, London, United Kingdom (2019)

    Google Scholar 

  9. Wei, K., Li, J., Ding, M., Ma, C., et al.: Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454–3469 (2020)

    Google Scholar 

  10. Qi, T., Wu, F., Wu, C., Lyu, L., et al.: FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning. arXiv: 2206.03200 (2022)

    Google Scholar 

  11. Wei, S., Tong, Y., Zhou, Z., Song, T.: Efficient and fair data valuation for horizontal federated learning. In: Yang, Q., Fan, L., Yu, H. (eds.) Federated Learning. Lecture Notes in Computer Science, vol. 12500, pp. 139–152. Springer, Cham. (2020)

    Chapter  Google Scholar 

  12. Johnson, D.S., Lorenz, W., Taenzer, M., Mimilakis, S., et al.: DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection. arXiv: 2102.08833v3 (2021)

    Google Scholar 

  13. Sun, T., Li, D., Wang, B.: Decentralized Federated Averaging. arXiv: 2104.11375 (2021)

    Google Scholar 

  14. Konecný, J., McMahan, H,B., Yu, F,K., Richtárik, P., et al.: Federated learning: strategies for improving communication efficiency. In: NIPS Workshop on Private Multi-Party Machine Learning. arXiv: 1610.05492 (2016)

    Google Scholar 

  15. Nilsson, A., Smith, S., Ulm, G., et al.: A performance evaluation of federated learning algorithms. In: Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning, DIDL ‘18, pp. 1–8. Association for Computing Machinery, Rennes, France (2018)

    Google Scholar 

  16. Zhang, H., Bosch, J., Olsson, H.: Federated learning systems: architecture alternatives. In: 27th Asia-Pacific Software Engineering Conference (APSEC), pp. 385–394. IEEE, Singapore (2020)

    Google Scholar 

  17. Zhu, H., Xu, J., Liu, S., Jin, Y.: Federated Learning on Non-IID Data: A Survey. arXiv: 2106.06843 (2021)

    Google Scholar 

  18. Dong, M.: Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification. arXiv: 1802.09697 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lakshya Gupta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, L., Meedinti, G.N., Popat, A., Perumal, B. (2023). Music Genre Classification Using Federated Learning. In: So-In, C., Londhe, N.D., Bhatt, N., Kitsing, M. (eds) Information Systems for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 324. Springer, Singapore. https://doi.org/10.1007/978-981-19-7447-2_23

Download citation

Publish with us

Policies and ethics