AI-Powered Music Analysis System for Lyric Interpretation and Metaphor Detection

Music is an integral part of human culture and emotional expression. The advent of Artificial Intelligence (AI) and Natural Language Processing (NLP) has revolutionized the way people analyze, interpret, and interact with music. While traditional music recommendation systems focus primarily on audio features, genre classification, and user preferences, they fail to capture the emotional depth, hidden meanings, and poetic expressions embedded in song lyrics. This research introduces ‘Rhythm Decode,’ an AI- powered system that leverages NLP techniques to analyze lyrical content, enabling metaphor detection, sentiment analysis, and deep interpretation of song lyrics. By implementing state-of-the-art deep learning models, ‘Rhythm Decode’ identifies underlying emotions, thematic patterns, and hidden literary devices within lyrics, offering a richer and more personalized music experience. The system extracts key sentiments such as joy, sadness, anger, and nostalgia while detecting metaphorical language and poetic structures often overlooked by conventional algorithms. The study outlines the dataset collection process, pre-processing techniques, model selection, feature extraction, evaluation metrics, and system implementation. Various machine learning and deep learning techniques, including Recurrent Neural Networks (RNNs), Transformers, and BERT-based models, are explored to enhance the accuracy of lyrical sentiment analysis. The results demonstrate that AI-powered lyrical analysis provides deeper contextual understanding, making music recommendations more intuitive and emotionally resonant. Additionally, the study highlights key challenges such as handling polysemy in lyrics, detecting abstract metaphors, and dealing with subjective interpretations of emotions. It also discusses potential biases in training data, computational constraints, and limitations in multilingual lyric processing. This research contributes to the growing field of AI-driven music analysis, bridging the gap between computational linguistics and musicology. Future work aims to expand the model’s capabilities to incorporate multilingual lyrics, cultural variations, and enhanced contextual understanding using multi-modal AI approaches. The findings from this study pave the way for next-generation music recommendation systems, offering listeners a profound and immersive musical experience beyond traditional metadata-based filtering.