NOTE DETECTION FOR SAZ WITH HARMONIC PRODUCT SPECTRUM METHOD

ZAFER OZCAN

Informasi Umum

Kode

15.04.1187

Klasifikasi

621.382 - Communications engineering, Analog, Digital, Electronic communications, Telecommunications, Digital data and telecommunications engineering

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Telecommunication

Dilihat

91 kali

Informasi Lainnya

Abstraksi

This research is about automatic note detection for Turkish instrument “Saz”. The Harmonic Product Spectrum (HPS) method is used for detection. The reason of doing this research is that so many applications are created for usage of music industry. However there are only few research on Turkish traditional instruments. Today, even if we don’t have the notes written on a paper we can extract the notes with a software. This is possible for many instruments, such as guitar, piano, clarinet etc. however there is no application created to detect notes of Saz yet. Therefore this research is important for increasing the usage of Saz by Musicians.
In this research, Harmonic Product Spectrum (HPS) method is used as feature extraction method. HPS applies down sampling to the input data in spectrum format then multiplies the each result of the down sampled input. Saz Notes are recorded in Signal Laboratory as Wav file. A Matlab based algorithm is used to simulate the system. Notes are fed to the system offline. Firstly, the input is preprocessed and converted to feasible form for HPS. Secondly, preprocessed data is fed to the HPS. HPS extracts the feature of the note. The feature is consist of two frequency value. Finally the features are compared with the training data with k-NN method to identify the note. In experiments we achieved 97% accuracy for noiseless notes and 89% accuracy for noisy notes. The software is able to extract the feature around 0.01 second. Overall computation time is around 0.0603 second. In a very similar research, done by Paul M. Brossier1, the accuracy is 96% and the computation time of their software is 0.3 second. In another research, done by Chris Duxbury2, the accuracy for onset detection was 92%. The common accuracy level for detection researches about musical instruments varies from 92% to 98%. Therefore the accuracy level of our research can be labeled as very satisfying.
Keywords: Saz, Harmonic Product Spectrum, Pitch, Note Detection