Comparison of spectrogram scaling in multi-label Music Genre Recognition

Classifying music into separate genres is an important task, which helps listeners discover new tracks, allows streaming services to better adjust to user preferences, and allows labels and music stores to advertise new albums more effectively. As the accessibility and ease-of-use of digital audio workstations increases, so does the quantity of music available to the public; additionally, differences between genres are not always well defined and can be abstract, with numerous records representing widely varying combinations of genres. In this article, multiple preprocessing methods and approaches to model training are described and compared, accounting for the eclectic nature of today's music. A custom and manually labeled dataset of more than 18000 entries has been used to perform the experiments.