Music Genre Classification based on Bidirectional-Long Short Term Memory Combined Convolutional Neural Networks

Music is known to be time series data, where the increase in the data size pose a significant challenge to build a robust music genre classification system. The robust system requires large amount of labelled music data and necessitates the requirement of capturing effective data features for enhanced classification of music genre. The proposed research focused on developing a Deep Learning (DL) framework for classification with four steps. Initially, the music labelled data is collected from the GTZAN and ballroom dataset. The collected data is pre-processed using normalization for equalizing the volume of all samples from the database to achieve a constant level. The pre-processed data undergoes feature extraction to extract Mel-Frequency Cepstral Coefficients (MFCCs). These features obtain characteristics of an audio signal that describes the short-term power spectrum on the mel scale of the audiobook signals. The extracted features are successfully classified using Bidirectional-Long Short Term Memory combined Convolutional Neural Network (Bi-LSTM-CNN). The proposed Bi-LSTM-CNN achieved 98.79% accuracy, 97.84% precision, and 97.05% f1-score which surpassed existing DL approach such as Masked Predictive Encoder- Recurrent Neural Network (MPE-RNN) and VGG16- Bi-LSTM.