Machine learning based classification of violin and viola instrument sounds for the same notes

Basit Öğe Kaydı
dc.authorid0000-0003-1840-9958
dc.authorid0000-0001-5040-3594
dc.contributor.authorAvcı, Kemal
dc.contributor.authorArıcan, Murat
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T18:51:31Z
dc.date.available2021-06-23T18:51:31Z
dc.date.issued2018
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionAselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netasen_US
dc.description26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780en_US
dc.description.abstractIn this paper, a study on the classification of violin and viola instrument sounds in the same notes that are hard for people to distinguish is presented. First, 16 statistical features defined in the time and frequency domain for 512 violin note recordings and 512 viola note recordings are extracted. Classification algorithms including Linear Distinction Analysis (LDA), k-nearest neighbors (k-NN), Support Vector Machines (SVM), and Random Forests (RF) classifiers are used to recognize violin and viola instrument sounds. The classification operation is performed on three different sets of features: only time domain features (12), only frequency domain features (4), and both time domain and frequency domain features (16). Simulation results show that the highest accuracy rate is obtained when using the Random Forests classifier with both time and frequency domain attributes. With Random Forest classifier, while obtaining 64.4% classification accuracy with 10 times cross-validity using only time domain and only frequency domain features, 79.6% classification success is achieved with both time and frequency domain features. © 2018 IEEE.en_US
dc.description.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8404422
dc.identifier.doi10.1109/SIU.2018.8404422
dc.identifier.endpage4en_US
dc.identifier.isbn9781538615010
dc.identifier.scopus2-s2.0-85050823518en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1109/SIU.2018.8404422
dc.identifier.urihttps://hdl.handle.net/20.500.12491/3840
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.institutionauthorAvcı, Kemal
dc.institutionauthorArıcan, Murat
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof26th IEEE Signal Processing and Communications Applications Conference, SIU 2018en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature Extractionen_US
dc.subjectInstrument Classificationen_US
dc.subjectMachine Learningen_US
dc.subjectMusic Signal Processingen_US
dc.subjectRandom Forestsen_US
dc.subjectViolaen_US
dc.subjectViolinen_US
dc.titleMachine learning based classification of violin and viola instrument sounds for the same notesen_US
dc.title.alternativeKeman ve viyola çalgı seslerinin aynı notalar için makine öğrenme tabanlı sınıflandırılması
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
kemal-avcı.pdf
Boyut:
423.03 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam metin/ Full text
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu