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Ali Cenk Gedik

dblp:87/2949 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2010
0000-0003-2769-0753ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing
music information retrieval
0.112010
Three Dimensions of Pitched Instrument Onset Detection · IEEE Trans. Speech Audio Process. 2010
Audio and music processing › music analysis
onset detection
0.112010
Three Dimensions of Pitched Instrument Onset Detection · IEEE Trans. Speech Audio Process. 2010

Methods — techniques the papers use, named apart from their topics

group-delay · 0.1decision-level fusion · 0.1
YearPublicationVenuePosition
2010 Pitch-frequency histogram-based music information retrieval for Turkish music
Ali Cenk Gedik, Baris Bozkurt
Signal Process.1
2010 Three Dimensions of Pitched Instrument Onset Detection
abstract
In this paper, we suggest a novel group delay based method for the onset detection of pitched instruments. It is proposed to approach the problem of onset detection by examining three dimensions separately: phase (i.e., group delay), magnitude and pitch. The evaluation of the suggested onset detectors for phase, pitch and magnitude is performed using a new publicly available and fully onset annotated database of monophonic recordings which is balanced in terms of included instruments and onset samples per instrument, while it contains different performance styles. Results show that the accuracy of onset detection depends on the type of instruments as well as on the style of performance. Combining the information contained in the three dimensions by means of a fusion at decision level leads to an improvement of onset detection by about 8% in terms of F-measure, compared to the best single dimension.
Andre Holzapfel, Yannis Stylianou, Ali Cenk Gedik, Baris Bozkurt
IEEE Trans. Speech Audio Process.3