Articles liés à A personalized content-based music similarity function
Synopsis
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.
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A personalized content-based music similarity function
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Scholars' Press Sep 2017, 2017
ISBN 10 : 620230152X
ISBN 13 : 9786202301527
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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. 76 pp. Englisch. N° de réf. du vendeur 9786202301527
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A personalized content-based music similarity function
impression à la demandeVendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. N° de réf. du vendeur 9786202301527
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A personalized content-based music similarity function
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Scholars\' Press, 2017
ISBN 10 : 620230152X
ISBN 13 : 9786202301527
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Poddigue LucaThe author graduated in Engineering of Computing Systems at Politecnico di Milano (Italy), where he studied and researched machine learning algorithms, data mining and processing of multimedia signals. He s currently a R. N° de réf. du vendeur 176461854
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A personalized content-based music similarity function
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Paperback. Etat : Brand New. 76 pages. 8.66x5.91x0.18 inches. In Stock. N° de réf. du vendeur zk620230152X
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A personalized content-based music similarity function
Edité par
Scholars' Press Sep 2017, 2017
ISBN 10 : 620230152X
ISBN 13 : 9786202301527
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. 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. N° de réf. du vendeur 9786202301527
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A personalized content-based music similarity function
Vendeur : preigu, Osnabrück, Allemagne
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Taschenbuch. Etat : Neu. A personalized content-based music similarity function | Luca Poddigue | Taschenbuch | 76 S. | Englisch | 2017 | Scholars' Press | EAN 9786202301527 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 110091882
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