A personalized content-based music similarity function

Language: English

Published by Scholars' Press Sep 2017, 2017

620230152X / 9786202301527

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This item is printed on demand - Print on Demand Titel. Neuware -Modeling music similarity is useful for a great variety of applications in everyday life: music recommendation, thematic playlists generation and music libraries organization are just some examples. The main goal is to find a method to automatically evaluate how much two generic audio tracks are similar. In this book, we devise an adaptive non-linear method to model the concept of music similarity. Our system collects similarity evaluations from each user, and exploits these data to fit personalized similarity functions. To this end, we exploit the content-based information inherently contained in music tracks, modeled by means of suitable low-level features. We rely on two strongly consolidated techniques: Multidimensional Scaling (MDS), a feature embedding method, and Support Vector Regression (SVR), a robust regression approach that can be made non-linear by using suitable kernel functions. The result is a novel system that is able to learn the similarity concept as it is perceived by each user, thus providing more accurate and user-tailored evaluations.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch.…

Seller Inventory # 9786202301527

Title
A personalized content-based music similarity function
Author
Luca Poddigue
Publisher
Scholars' Press Sep 2017
Publication year
2017
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
620230152X
ISBN 13
9786202301527
Item weight
131 grams
Dimensions
220x150x6 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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