Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Research Project Klasik Türk Müziği Kayıtlarının Otomatik Olarak Notaya Dökülmesi ve Otomatik Makam Tanıma(2010) Bozkurt, Barış; Savacı, Ferit Acar; Karaosmanoğlu, Mustafa KemalBu projede Klasik Türk müziği kayıtlarının otomatik olarak notaya dökülmesi ve makamların otomatik olarak tanınması için literatürde ilk defa kullanılan yöntem ve teknikler önerilmiş, yazılımlar gerçeklenmiştir. Bu amaçlara ulaşabilmek için bir dizi problem derinlemesine incelenmiştir. Öncelikle temel titreşim frekans(f0) analizi için literatürde varolan teknikler denenerek en uygun algoritma seçilmiştir. Bu algoritma ile elde edilen sonuçları iyileştirmek için bazı süzgeçler tasarlanmış ve önemli iyileştirmeler sağlanmıştır. Bunu takiben f0 bilgisinden f0 dağılımları(kullanım sıklıkları) elde edilmiş, f0 dağılımlarını kullanarak karar sesi tespiti, kuram - icra uyum düzeyi ölçümü ve otomatik makam tanıması yapan özgün araçlar tasarlanmıştır. Literatürde ilk defa 5 ayrı kuram ve 9 sık kullanılan makamdan güvenilir kayıtlar içeren veri setleri üzerinde kuram - icra uyum düzeyi detaylı olarak incelenmiştir. Yine ilk olarak birçok hesaplamalı müzikoloji çalışmasında kullanılabilecek sembolik bir Türk müziği veritabanı hazırlanmış ve paylaşıma açılmıştır. Otomatik notaya dökme uygulaması için gerekli olan başlangıç noktası tespit algoritması, f0 nicemleme yöntemi ve MIDI’ye dönüştürme araçları geliştirilmiştir.Conference Object Citation - WoS: 2Türk Makam Müziği Notaları için Otomatik Ezgi Bölütleme(Institute of Electrical and Electronics Engineers Inc., 2014) Bozkurt, Barış; Karaçalı, Bilge; Karaosmanoğlu, M. Kemal; Ünal, ErdemAutomatic melodic segmentation is one of the important steps in computational analysis of melodic content from symbolic data This widely studied research problem has been very rarely considered for Turkish makam music. In this paper we first present test results for state-of-the-art techniques from literature on Turkish makam music data Then, we present a statistical classification-based segmentation system that exploits the link between makant melodies and usul and makam scale hierarchies together with the well-known features in literature. We show through tests on a large dataset that the proposed system has a higher accuracy.Conference Object Citation - Scopus: 6Klasi̇k Türk Müzi̇ği̇ İ̇çin Otomati̇k Notaya Dökme Si̇stemi̇(Institute of Electrical and Electronics Engineers, 2011) Bozkurt, Barış; Gedik, Ali Cenk; Karaosmanoğlu, M. KemalThis study presents an automatic transcription system for Turkish music for the first time in literature. We first discuss the characteristics of Turkish music that are taken into consideration in the design of the system. Then, the following signal processing components of the system are described briefly in relation to each other and explaining their function in the system: f0 estimation, automatic tonic detection and makam recognition based on pitch distributions, frequency and duration quantization. © 2011 IEEE.Conference Object Citation - WoS: 1Citation - Scopus: 2Glottal Source Estimation Using an Automatic Chirp Decomposition(Springer, 2010) Drugman, Thomas; Bozkurt, Barış; Dutoit, ThierryIn a previous work, we showed that the glottal source can be estimated from speech signals by computing the Zeros of the Z-Transform (ZZT). Decomposition was achieved by separating the roots inside (causal contribution) and outside (anticausal contribution) the unit circle. In order to guarantee a correct deconvolution, time alignment on the Glottal Closure Instants (GCIs) was shown to be essential. This paper extends the formalism of ZZT by evaluating the Z-transform on a contour possibly different from the unit circle. A method is proposed for determining automatically this contour by inspecting the root distribution. The derived Zeros of the Chirp Z-Transform (ZCZT)-based technique turns out to be much more robust to GCI location errors. © 2010 Springer-Verlag.Conference Object Citation - WoS: 16Citation - Scopus: 46Complex Cepstrum-Based Decomposition of Speech for Glottal Source Estimation(International Speech Communication Association, 2009) Drugman, Thomas; Bozkurt, Barış; Dutoit, ThierryHomomorphic analysis is a well-known method for the separation of non-linearly combined signals. More particularly, the use of complex cepstrum for source-tract deconvolution has been discussed in various articles. However there exists no study which proposes a glottal flow estimation methodology based on cepstrum and reports effective results. In this paper, we show that complex cepstrum can be effectively used for glottal flow estimation by separating the causal and anticausal components of a windowed speech signal as done by the Zeros of the Z-Transform (ZZT) decomposition. Based on exactly the same principles presented for ZZT decomposition, windowing should be applied such that the windowed speech signals exhibit mixed-phase characteristics which conform the speech production model that the anticausal component is mainly due to the glottal flow open phase. The advantage of the complex cepstrum-based approach compared to the ZZT decomposition is its much higher speed.Conference Object Citation - WoS: 3Citation - Scopus: 6Phase-Based Methods for Voice Source Analysis(Springer Verlag, 2007) D’Alessandro, Christophe; Bozkurt, Barış; Doval, Boris; Dutoit, Thierry; Henrich, Nathalie; Tuan, Vu Ngoc; Sturmel, NicolasVoice source analysis is an important but difficult issue for speech processing. In this talk, three aspects of voice source analysis recently developed at LIMSI (Orsay, France) and FPMs (Mons, Belgium) are discussed. In a first part, time domain and spectral domain modelling of glottal flow signals are presented. It is shown that the glottal flow can be modelled as an anticausal filter (maximum phase) before the glottal closing, and as a causal filter (minimum phase) after the glottal closing. In a second part, taking advantage of this phase structure, causal and anticausal components of the speech signal are separated according to the location in the Z-plane of the zeros of the Z-Transform (ZZT) of the windowed signal. This method is useful for voice source parameters analysis and source-tract deconvolution. Results of a comparative evaluation of the ZZT and linear prediction for source/tract separation are reported. In a third part, glottal closing instant detection using the phase of the wavelet transform is discussed. A method based on the lines of maximum phase in the time-scale plane is proposed. This method is compared to EGG for robust glottal closing instant analysis.