Music Emotion Recognition and Classification Using Hybrid CNN-LSTM Deep Neural Network

In music information retrieval (MIR), emotion-based classification is a complex and challenging task for researchers. In modern-day information technology, understanding music emotions through human-computer interaction plays a vital role in capturing the attention of both researchers and the music industry. This paper presents a learning algorithm by adopting the decision tree, random forest, k-nearest neighbors, multi-layer perceptron (MLP), long short-term memory (LSTM) neural network, convolutional neural networks (CNNs), and CNN-LSTM hybrid deep learning approaches with relevant feature extraction techniques. Many researchers have performed emotion recognition in music for different languages such as English, Chinese, Spanish, Turkish, Hindi, etc. However, languages like Assamese have drawn very little attention in the research of music emotion recognition (MER). This work aims to perform a novel approach to emotion recognition in Assamese songs. In this study, a newly created Assamese dataset of 200 song samples is used with four different emotions and another dataset used is the RAVDESS emotional song database which consists of 1012 song samples with six different music emotions. Relevant features such as mel-frequency cepstrum coefficients (MFCC), mel spectrogram, and chroma features are extracted from the song samples to investigate the performance of the proposed method. A comparative analysis using different classifiers is carried out and the findings of this study suggest that the CNN-LSTM model has shown better accuracy with both datasets. The accuracy is 85.00% with the Assamese dataset, and with the RAVDESS dataset, the accuracy is 89.66% compared to the other classifiers used in this work.