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dc.contributor.authorSchmitt, Maximilian
dc.contributor.authorSchuller, Björn
dc.contributor.editorEibl, Maximilian
dc.contributor.editorGaedke, Martin
dc.date.accessioned2017-08-28T23:46:59Z
dc.date.available2017-08-28T23:46:59Z
dc.date.issued2017
dc.identifier.isbn978-3-88579-669-5
dc.identifier.issn1617-5468
dc.description.abstractThe recognition of audio effects employed in recordings of electric guitar or bass has a wide range of applications in music information retrieval. It is meaningful in holistic automatic music transcription and annotation approaches for, e. g., music education, intelligent music search, or musicology. In this contribution, we investigate the relevance of a large variety of state-of-the-art acoustic features for the task of automatic guitar effect recognition. The usage of functionals, i. e., statistics such as moments and percentiles, is hereby compared to the bag-of-audio-words approach to obtain an acoustic representation of a recording on instance level. Our results are based on a database of more than 50 000 monophonic and polyphonic samples of electric guitars and bass guitars, processed with 10 different digital audio effects.en
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-275
dc.subjectGuitar Effects
dc.subjectMusic Information Retrieval
dc.subjectBag-of-Audio-Words
dc.titleRecognising Guitar Effects - Which Acoustic Features Really Matter?en
mci.reference.pages177-190
mci.conference.sessiontitleMusik trifft Informatik
mci.conference.locationChemnitz
mci.conference.date25.-29. September 2017
dc.identifier.doi10.18420/in2017_12


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