Classifying musical genres through deep learning and advanced audio analysis methods

Music genre classification has become a big task in the age of seamless music streaming services and content development. Accurate music genre classification is critical for applications such as music recommendations, content organization and identifying musical trends. This study proposes a comprehensive approach to music genre classification based on deep learning and advanced audio analysis tools. To address the challenge of accurately classifying music genres for music apps, a deep learning model is used with the GTZAN dataset for classification. The study investigates on various algorithms like Convolutional Neural Networks (CNN), Feedforward Neural Networks (FNN), Support Vector Machines (SVM), k-Nearest Neighbor (kNN) and Long Short-term Memory (LSTM) on this dataset. The study involves cross-validating the models output after feature extraction from the preprocessed audio data and evaluating its performance. This paper proposes an enhanced CNN model which outperforms regular neural networks by taking advantage of its capacity to record complex spectral patterns. These findings show how deep learning algorithms can improve systems for categorizing music genres, potentially benefiting music-related apps and interfaces. Hence, the proposed model has given an accuracy of 94.2% on the GTZAN dataset and 93.3% on the ISMIR2004 Ballroom dataset.