Music Information Retrieval and Audio Content Processing in the Age of Artificial Intelligence: Techniques, Challenges, and Emerging Applications

Abstract

The field of Music Information Retrieval (MIR) and audio content processing have become an important area of research concern in the age of Artificial Intelligence (AI), responding to a need to make the ever-increasing music archives stored and recovered automatically through analysis, indexing and search. In this research, we attempt to collate an all-inclusive review of the AI approaches that have changed the face of the establish MIR tasks such as genre classification, instrument identification, mood identification and the music suggestion. It discusses recent methods of feature extraction, including the use of Mel-Frequency Cepstral Coefficients (MFCCs) and deep spectral embeddings, and representation learning methods made available by convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The review of the recent work shows that AI models with their dramatic superiority over traditional approaches to relying on handcrafted features open great opportunities regarding accuracy, generalization, and robustness in a broad range of datasets. Crucial issues, including the scarcity of labeled data, domain adaptation, model interpretability, and the intellectual property are critically addressed. There are also examples of emerging applications covered by the paper such as AI-aided music composition, adaptive streaming and real time audio analytics in interactive systems. The review ends by providing future research orientations based on explainable AI, multimodal combination of role, audio-lyric-visual data input and deployment of resources on low-powered edge devices. This synthesis can be used as a reference to researchers and practitioners alike in the industry that seeks to create scalable, accurate, and ethically responsible MIR systems during times of the AI revolution.