CNN Performance in Genre Classification on Popular Genres from Amazonia Region: An Analysis in Relation to Human Evaluation and its Implications

Music is one of the most useful means of communication for social organization, influencing coexistence, customs, lifestyle and tastes. Noticeable in different musical genres, whether for: dancing, celebrating, resting, helping in moments of loneliness, sadness, etc. This work presents a dataset containing the musical characteristics of the most popular genres from the Amazonia region, from countries of Bolívia, Brazil, Colômbia, Equador, French Guiana, Peru, Dominican Republic, and Venezuela, with 9 popular music genres: andean, brega, carimbó, cúmbia, merengue, pasillo, salsa, vaqueirada, and zouk. This dataset has 22500 samples, 2500 for each genre, segmented in 5 portions of 20 seconds, represented by mel spectrogram. All tracks were acquired by the YouTube or Spotify platform considering viewing rate and sound quality. To evaluate the AMPOP dataset, a custom CNN model was developed for genre classification. Its performance, measured in terms of precision and accuracy, was compared against several state-of-the-art architectures, including MobileNet, ResNet, VGG, EfficientNet, Xception, MobileViT, and MaxViT. Finally, the classification performance of music genre specialists was analyzed based on more than 500 musical evaluations. It was observed that the proposed model significantly outperformed the human experts; however, this difference may be attributed to the inherent subjectivity of human perception as well as the cultural and contextual complexity of the analyzed genres. This highlights the importance of computational approaches to support music genre classification.