Music information retrieval (MIR) refers to the interdisciplinary field that focuses on the extraction, organization, and utilization of various types of music-related information. It encompasses the study and development of techniques and methodologies to understand and analyze music, as well as to retrieve relevant information from music databases or collections.
In MIR, information can refer to different aspects of music, such as pitch, rhythm, melody, lyrics, timbre, genre, emotion, or even cultural and social contexts. The retrieval of this information involves the use of computational methods, algorithms, and data analysis techniques to process and interpret music in order to derive meaningful insights. MIR draws on various domains, including computer science, engineering, acoustics, musicology, psychology, and cognitive science.
The goal of MIR is to facilitate the efficient organization and access to music-related data, enabling tasks such as music recommendation, music similarity, automatic music transcription, music classification, music recommendation, and music recommendation systems. MIR methods can involve audio signal processing, machine learning, pattern recognition, and data mining approaches.
Overall, music information retrieval aims to bridge the gap between the rich and complex domain of music and the capabilities of computational tools, enabling researchers, musicians, musicologists, industry professionals, and even casual listeners to explore, analyze, and benefit from the wealth of information embedded in music.
