Segment-based evaluation of music genre classification models with the BYRM Dataset - Guimarães, Victória; Kienen, João Gustavo; de Freitas, Rosiane (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2025)
The increasing use of deep learning in music information retrieval has driven progress in music genre classification, yet many studies overlook the temporal structure of music. Most benchmark datasets provide only a single, fixed excerpt per song, limiting the investigation of how time-related factors influence classification. In this work we introduce the Brazilian YouTube Regional Music (BYRM) Dataset, a curated collection of Brazilian regional music comprising ten genres, with multiple excerpts extracted from different parts of each track. The dataset supports controlled experiments on how both excerpt position and segment duration affect model performance. BYRM includes vectorized features, preprocessed spectrograms, and metadata for reproducibility. To evaluate the dataset, we conduct experiments using a Vision Transformer (ViT), supported by SVM and ResNet50 baselines. Results show that excerpts from the middle of the song (e.g., 90 to 120 seconds) yield better performance, and that optimal segment duration varies by genre. BYRM enables fine-grained analysis of genre-specific temporal patterns and supports future research on temporal modeling and genre similarity.