Hybrid Deep Learning-Based Automated Genre Classification of Assamese Regional Songs

This work aims to preserve and promote the rich musical heritage of Assam by developing an automated classification system for Assamese regional songs using a hybrid deep learning approach. This method not only modernizes the preservation of traditional music but also enhances its accessibility to a global audience for integrating AI with cultural conservation. Five genres of Assamese songs—Bihu, Kamrupiya Lokageet, Goalporiya Lokageet, Borgeet, and Naam—are considered in this study. By leveraging Convolutional Neural Networks (CNNs) and advanced audio feature extraction techniques such as Mel-Frequency Cepstral Coefficients (MFCCs) and spectrograms, a hybrid model combining VGG16 and ResNet50 is developed. This fusion utilizes the strengths of both architectures, enhancing the model’s performance and accuracy. Following the process, it is observed that two distinctly different genres, Bihu and Borgeet, are accurately categorized by the proposed model, while the remaining three show slight labeling inconsistencies.