Raga Analysis Using Artificial by Prasoon Pranay (4 results)

Taschenbuch. Condition: Neu. Raga Analysis Using Artificial Neural Network | Pranay Prasoon (u. a.) | Taschenbuch | 72 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659620393 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Because music conveys and evokes feelings, a wealth of research has been performed on music emotion recognition. Research has shown that musical mood is linked to features based on rhythm, timbre, melody and lyrics. For example, sad music correlates with slow tempo while happy music is generally faster. We see only limited success has been obtained in learning automatic classifiers of Hindustani classical music emotions. In this book we have collected a ground truth data set of 196 raga clips that have been tagged with one of two emotions 'happy' and 'sad'. We investigated all recordings of a time period of 30 seconds for uniformity. Various set of audio features were extracted using standard algorithms. A musical mood classifier was trained. We found that the probability of pitch contour, when included as one of the features, gives 30% higher accuracy of mood recognition. 72 pp. Englisch. …

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Prasoon PranayDr. Soubhik Chakraborty is an Associate Professor in the Deptt. of Applied Mathematics at BIT Mesra, Ranchi, India. He has published several papers in algorithm analysis and music analysis. He is an AMS, ACM and IEEE re.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Because music conveys and evokes feelings, a wealth of research has been performed on music emotion recognition. Research has shown that musical mood is linked to features based on rhythm, timbre, melody and lyrics. For example, sad music correlates with slow tempo while happy music is generally faster. We see only limited success has been obtained in learning automatic classifiers of Hindustani classical music emotions. In this book we have collected a ground truth data set of 196 raga clips that have been tagged with one of two emotions 'happy' and 'sad'. We investigated all recordings of a time period of 30 seconds for uniformity. Various set of audio features were extracted using standard algorithms. A musical mood classifier was trained. We found that the probability of pitch contour, when included as one of the features, gives 30% higher accuracy of mood recognition.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch.…