Music Similarity and Retrieval: An Introduction to Audio- by Peter Knees,Markus Schedl

By Peter Knees,Markus Schedl

This booklet presents a precis of the manifold audio- and web-based methods to tune info retrieval (MIR) examine. unlike different books dealing completely with track sign processing, it addresses extra cultural and listener-centric features and therefore presents a extra holistic view. for that reason, the textual content comprises tools working on positive aspects extracted without delay from the audio sign, in addition to equipment working on positive aspects extracted from contextual info, both the cultural context of tune as represented on the internet or the person and utilization context of music.

Following the established document-centered paradigm of data retrieval, the ebook addresses types of track similarity that extract computational positive factors to explain an entity that represents track on any point (e.g., tune, album, or artist), and strategies to calculate the similarity among them. whereas this attitude and the representations mentioned can't describe all musical dimensions, they allow us to successfully locate track of comparable features by means of offering summary summarizations of musical artifacts from varied modalities.

The textual content handy presents a entire and obtainable advent to the themes of tune seek, retrieval, and advice from an instructional viewpoint. it is going to not just enable these new to the sector to speedy entry MIR from a knowledge retrieval standpoint but additionally bring up know-how for the advancements of the song area in the larger IR neighborhood. during this regard, half I offers with content-based MIR, specifically the extraction of positive factors from the track sign and similarity calculation for content-based retrieval. half II as a result addresses MIR tools that utilize the digitally obtainable cultural context of track. half III addresses equipment of collaborative filtering and user-aware and multi-modal retrieval, whereas half IV explores present and destiny functions of tune retrieval and recommendation.>

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