Comparison of spectrogram scaling in multi-label Music Genre Recognition
This work addresses the problem of accurately classifying music genres for listeners and platforms, but it is incremental as it focuses on comparing existing methods rather than introducing new ones.
The paper tackled the challenge of multi-label music genre recognition by comparing various spectrogram scaling preprocessing methods and model training approaches, using a custom dataset of over 18,000 entries to evaluate their effectiveness.
As the accessibility and ease-of-use of digital audio workstations increases, so does the quantity of music available to the average listener; additionally, differences between genres are not always well defined and can be abstract, with widely varying combinations of genres across individual records. In this article, multiple preprocessing methods and approaches to model training are described and compared, accounting for the eclectic nature of today's albums. A custom, manually labeled dataset of more than 18000 entries has been used to perform the experiments.