Lightweight convolutional neural network architecture design for music genre classification using evolutionary stochastic hyperparameter selection

Abstract

Convolutional neural networks (CNNs) have succeeded in various domains, including music information retrieval (MIR). Music genre classification (MGC) is one such task in the MIR that has gained attention over the years because of the massive increase in online music content. Accurate indexing and automatic classification of these large volumes of music content require high computational resources, which pose a significant challenge to building a lightweight system. CNNs are a popular deep learningbased choice for building systems for MGC. However, finding an optimal CNN architecture for MGC requires domain knowledge both in CNN architecture design and music. We present MGA-CNN, a genetic algorithm-based approach with a novel stochastic hyperparameter selection for finding an optimal lightweight CNN-based architecture for the MGC task. The proposed approach is unique in automating the CNN architecture design for the MGC task. MGA-CNN is evaluated on three widely used music datasets and compared with seven peer rivals, which include three automatic CNN architecture design approaches and four manually designed popular CNN architectures. The experimental results show that MGA-CNN surpasses the peer approaches in terms of classification accuracy, parameter numbers, and execution time. The optimal architectures generated by MGA-CNN also achieve classification accuracy comparable to the manually designed CNN architectures while spending fewer computing resources.