Why use Transformers?

Music genre classification is a challenging and complex task that involves several steps like genre analysis, embedding, and categorization of music tracks into distinct genres. All these steps involve several large calculations, which are time- and memory-consuming. Many other methods of music genre classification have already been tested, but recent studies showed that the Transformers module can effectively handle all the complex steps associated with music genre classification. The essence of using transformers in music genre classification lies in their ability to capture intricate patterns, dependencies, and temporal relationships within music data. Unlike other methods, which often struggle to represent the rich and complex structures of music, transformers outperform in modelling sequences of data. It can analyze raw audio, symbolic notation, or even textual descriptions to identify the underlying genre with remarkable accuracy.

All the animals have a reaction to music. Music is a special thing that has an effect directly on our brains. Humans are the creators of different types of music, like pop, hip-hop, rap, classical, rock, and many more. Specifically, music can be classified by its genres. Our brains can detect different genres of music by default, but computers don’t have this mechanism. But music genre classification has vast usage in recommendation systems, content organization, and the music industry as well. To analyze music genres, we can use machine learning. In this article, we will discuss how we can utilize transformer-based models to perform music genre classification.