Music Emotion Recognition using Convolutional Neural Networks for Regional Languages

Music emotion recognition is a challenging task that has been getting increasing kindness in the field of artificial intelligence. We propose a unique lD Convolutional Neural Network (lD CNN) method for music emotion recognition. Our proposed approach combines the advantages of convolutional neural networks with the lD temporal structure of music signals. Evaluate our model on two publicly available datasets and demonstrate its effectiveness in accurately recognizing different emotions from music signals. Our method presents a novel approach to abstracting raw audio signals into meaningful representations. It utilizes multiple lD convolutional and pooling layers to extract features from the raw audio signals, which can then be used for further analysis. We demonstrate the effectiveness of our approach by providing examples of applications that can benefit from this abstraction process, such as sound recognition and speech recognition. Additionally, we discuss the potential of this method to be applied in other areas such as natural language processing and music generation. We compare the performance of our model with traditional machine learning (ML) methods and demonstrate that our lD CNN outperforms these traditional ML methods in terms of accuracy. We also discuss potential use cases for this technology and its implications for future research in audio signal processing. We show that our model achieves superior results in terms of accuracy and computational efficiency, making it a promising solution for music emotion recognition tasks. Our results suggest that the lD CNN approach is an effective tool for recognizing musical emotions with high accuracy