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An evaluation of sound event classification in an active learning setting: Support Vector Machines versus Random Forest Classification
Högskolan i Skövde, Institutionen för informationsteknologi.
2019 (Engelska)Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
Abstract [en]

The process of labeling data can be work intensive, not least in the area of sound event classification. Active learning can ease this intensive process and still provide an acceptable performance by a classifier. In this thesis, the performance of the classification of sound events in an active learning setting is evaluated in terms of robustness w.r.t. bias in the data. An active learning algorithm for evaluating sound event data is built and an experiment is performed. The result is that with the used feature extraction method, there is no difference in performance between when active learning is used and when active learning is not used. Also, there is no difference between the sample selection methods random, margin and entropy selection in this scenario.

Ort, förlag, år, upplaga, sidor
2019. , s. 21
Nyckelord [en]
active learning, sound event classification, robustness, biased data, SVM, random forest
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:his:diva-17862OAI: oai:DiVA.org:his-17862DiVA, id: diva2:1376155
Ämne / kurs
Informationsteknologi
Utbildningsprogram
Data Science - magisterprogram
Handledare
Examinatorer
Tillgänglig från: 2020-11-25 Skapad: 2019-12-08 Senast uppdaterad: 2020-11-25Bibliografiskt granskad

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