Lingua: Inglese
Editore: LAP Lambert Academic Publishing, 2012
ISBN 10: 384433260X ISBN 13: 9783844332605
Da: preigu, Osnabrück, Germania
EUR 43,30
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Classification of Pakistani Musical Instruments Using Soft Set | Saima Anwar Lashari (u. a.) | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783844332605 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2012
ISBN 10: 384433260X ISBN 13: 9783844332605
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 172,46
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Aggiungi al carrellopaperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2012
ISBN 10: 384433260X ISBN 13: 9783844332605
Da: moluna, Greven, Germania
EUR 41,67
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Lashari Saima AnwarSaima Anwar Lashari is a PhD student at Universiti Tun Hussein Onn Malaysia (UTHM). Her research interests are in the field of signal and image processing, data mining and soft set.The use of classification alg.
Lingua: Inglese
Editore: LAP Lambert Academic Publishing, 2012
ISBN 10: 384433260X ISBN 13: 9783844332605
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 49,59
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The use of classification algorithms to automatically classify musical instruments is a current and ongoing research. Musical instruments classification has many applications such as cataloging of sound samples, music transcription and annotation system. Though, audio classification comprise of interdisciplinary areas namely data mining, signal processing and musicology. Meanwhile, soft set theory has emerged as a new mathematical tool that has high potential to be applied in many directions. However, soft set for musical instrument classification has not been widely experimented although this method is very reliable in handling texture classification and for data analysis efficiently. Thus, this book provides a classification algorithm based on soft set incorporating Traditional Pakistani musical instruments and offers a new perspective for automatic musical instrument classification.