Vishnu Srinivasa Murthy, Y.V.Koolagudi, S.G.2026-02-052019ACM Computing Surveys, 2019, Vol.51, 3, p. -3600300https://doi.org/10.1145/3177849https://idr.nitk.ac.in/handle/123456789/28417A huge increase in the number of digital music tracks has created the necessity to develop an automated tool to extract the useful information from these tracks. As this information has to be extracted from the contents of the music, it is known as content-based music information retrieval (CB-MIR). In the past two decades, several research outcomes have been observed in the area of CB-MIR. There is a need to consolidate and critically analyze these research findings to evolve future research directions. In this survey article, various tasks of CB-MIR and their applications are critically reviewed. In particular, the article focuses on eight MIR-related tasks such as vocal/non-vocal segmentation, artist identification, genre classification, raga identification, query-by-humming, emotion recognition, instrument recognition, and music clip annotation. The fundamental concepts of Indian classical music are detailed to attract future research on this topic. The article elaborates on the signal-processing techniques to extract useful features for performing specific tasks mentioned above and discusses their strengths as well as weaknesses. This article also points to some general research issues in CB-MIR and probable approaches toward their solutions so as to improve the efficiency of the existing CB-MIR systems. 2018 Copyright is held by the owner/author(s). © 2018 Association for Computing Machinery. All rights reserved.Artist identificationIndian classical musicInstrument identificationMusic annotationMusic genreMusic mood estimationMusic recommendation systemMusic related featuresOpen problems in music information retrievalQuery-by-humming/singingSegmentation of vocal and non-vocal regionsSurvey of music information retrieval