Integração dos Coeficientes Cepstrais de Frequência Mel no Framework Mir_Ref

Abstract

With the growing consumption of multimedia content, the field of Music Information Retrieval (MIR) has established itself as a strategic area for the analysis and interpretation of audio signals. In this context, the extraction of audio features, such as timbre, plays a central role in several tasks, but significant challenges remain due to the complexity and diversity of musical signals. Despite advances in the field, there is still a gap regarding the systematic comparison between traditional feature extraction methods and modern approaches based on deep learning. In particular, the challenge persists of integrating and comparing different types of audio representations within a standardized and reproducible experimental environment. This study aims to investigate the feasibility of integrating Mel-frequency cepstral coefficients (MFCCs) into the mir_ref framework, enabling the inclusion of this classical representation in comparative experiments with modern methods, such as embeddings generated by deep neural networks. In addition to the inclusion of MFCCs, the quality of the representations extracted by different methods was evaluated within the same experimental framework, promoting reproducibility and standardization in MIR evaluation. The conducted experiments demonstrate the impact of representation choice on music classification tasks, highlighting the particularities and limitations of each approach. Therefore, this study contributes to a comparative understanding of traditional and modern techniques for audio representation.