Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/14333
Title: MELODY EXTRACTION FROM POLYPHONIC MUSIC SIGNALS BASED ON SYNCHROSQUEEZED WAVE PACKET TRANSFORM
Authors: Babubhai, Bhadrecha Bhanesh
Keywords: Digital technology;Music;Voicing Detection;Melody
Issue Date: Jun-2016
Publisher: Dept. of Electrical Engineering IITR
Abstract: Since the development in digital technology, music has a lead role in the leading tech- nological evolution. Music can be thought as basic human need. As huge amount of data is required to store and transfer audio signal, challenges arise as how e ciently one can store audio signal without almost no changes in its quality. With the use of cloud backup one can have own music collection, but this leads to wastage of band- width as the same music content can repeat for persons. So, to index and search these vast amounts of data becomes a challenging task. In melody extraction, the melody ( pre-dominant fundamental frequency of a music signal) is extracted from the music which then gives salient applications like indexing and searching music content, query by humming (QBH), voicing detection, voice separation, genre classi cation (like Rock, Pop, Indian etc...), music teaching (by extracting di erent beat frequencies), games, se- curity systems, karaoke system and more. So many melody extraction algorithms have been developed, but most of the algorithms depend on a type of music where for speci c music it can work e ectively. Melody extraction can be vocal or instrumental. Vocal melody extraction algorithms extract the human voice (Voicing detection) whilst instru- mental includes detection of particular instrument sound like tabla, guitar, ute etc. As synchrosqueezed wave packet transform gives very accurate time-frequency represen- tation, the need for extra steps to calculate melody is eliminated and directly giving nal melody. Proposed melody algorithm shows an improved accuracy compared some existing methods.
URI: http://hdl.handle.net/123456789/14333
metadata.dc.type: Other
Appears in Collections:DOCTORAL THESES (Electrical Engg)

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