Musical Genre Classification Using Advanced Audio Analysis and Deep Learning Techniques

Abstract

Classifying music genres has been a significant problem in the decade of seamless music streaming platforms and countless content creations. An accurate music genre classification is a fundamental task with applications in music recommendation, content organization, and understanding musical trends. This study presents a comprehensive approach to music genre classification using deep learning and advanced audio analysis techniques. In this study, a deep learning method was used to tackle the task of music genre classification. For this study, the GTZAN dataset was chosen for music genre classification. This study examines the challenge of music genre categorization using Convolutional Neural Networks (CNN), Feedforward Neural Networks (FNN), Support Vector Machine (SVM), k-nearest Neighbors (kNN), and Long Short-term Memory (LSTM) models on the dataset. This study precisely cross-validates the model's output following feature extraction from pre-processed audio data and then evaluates its performance. The modified CNN model performs better than conventional NN models by using its capacity to capture complex spectrogram patterns. These results highlight how deep learning algorithms may improve systems for categorizing music genres, with implications for various music-related applications and user interfaces. Up to this point, 92.7% of the GTZAN dataset's correctness has been achieved on the GTZAN dataset and 91.6% on the ISMIR2004 Ballroom dataset.

Keywords

• Convolutional neural networks
• long short-term memory
• support vector machine
• k-nearest neighbors
• genre classification