Music Genre Classification Capabilities of Enhanced Neural Network Architectures

Document Type

Article

Authors

Joshua Engelkes, University of Minnesota MorrisFollow

Abstract

With the increase of digital music audio uploads, applications that deal with music information have been widely requested by streaming platforms. Automatic music genre classification is an important function of music recommendation and music search applications. Since the music genre categorization criteria continually shift, data-driven methods such as neural networks have been proven especially useful to music information retrieval. An enhanced CNN architecture, the Bottom-up Broadcast Neural Network, uses mel-spectrograms to push music data through a network where important low-level information is preserved. An enhanced RNN architecture, the Independent Recurrent Neural Network for Music Genre Classification, takes advantage of the sequential aspects of music while combating typical RNN shortcomings. Both enhanced neural networks performed the best in their studies, but the IndRNN has a higher average classification accuracy.

Recommended Citation

Engelkes, Joshua (2024) "Music Genre Classification Capabilities of Enhanced Neural Network Architectures," Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal: Vol. 11: Iss. 1, Article 3.
DOI: https://doi.org/10.61366/2576-2176.1135
Available at: https://digitalcommons.morris.umn.edu/horizons/vol11/iss1/3

Primo Type

Article