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staff:vatolkin:publications [2020-08-27 08:50]
igor.vatolkin
staff:vatolkin:publications [2021-03-28 17:32]
igor.vatolkin
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 ===== Peer-Reviewed Conference Proceedings ===== ===== Peer-Reviewed Conference Proceedings =====
  
-<​html><​b><​font color=#​006633>​[32]</​font></​b>​ <i> I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Evolutionary ​Approximation ​of Instrumental Texture in Polyphonic Audio Recordings</​font></​b>​. Accepted for Proceedings of the IEEE World Congress on Computational Intelligence ​(WCCI), <font color=#​996600>​2020</​font></​html>​+<​html><​b><​font color=#​006633>​[35]</​font></​b>​ <i> I. Vatolkin, F. Ostermann, and M. Müller</​i>:<​b><​font color=#​0000FF> ​An Evolutionary ​Multi-Objective Feature Selection Approach for Detecting Music Segment Boundaries ​of Specific Types</​font></​b>​. Accepted for Proceedings of the Genetic and Evolutionary Computation Conference ​(GECCO), <font color=#​996600>​2021</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[31]</​font></​b>​ <i> P. Ginsel, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Analysis of Structural Complexity Features for Music Genre Recognition</​font></​b>​. ​Accepted for Proceedings of the IEEE World Congress on Computational Intelligence ​(WCCI), <font color=#​996600>​2020</​font></​html>​+<​html><​b><​font color=#​006633>​[34]</​font></​b>​ <i> I. Vatolkin, B. Adrian, J. Kuzmic</​i>:<​b><​font color=#​0000FF>​ A Fusion of Deep and Shallow Learning to Predict Genres Based on Instrument and Timbre Features</​font></​b>​. Accepted for Proceedings of the 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART),​ <font color=#​996600>​2021</​font></​html>​ 
 + 
 +<​html><​b><​font color=#​006633>​[33]</​font></​b>​ <i> I. Vatolkin, M. Koch, M. Müller</​i>:<​b><​font color=#​0000FF>​ A Multi-Objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation</​font></​b>​. Accepted for Proceedings of the 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART),​ <font color=#​996600>​2020</​font></​html>​ 
 + 
 +<​html><​b><​font color=#​006633>​[32]</​font></​b>​ <i> I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Evolutionary Approximation of Instrumental Texture in Polyphonic Audio Recordings</​font></​b>​. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, <font color=#​996600>​2020</​font></​html>​ 
 + 
 +<​html><​b><​font color=#​006633>​[31]</​font></​b>​ <i> P. Ginsel, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Analysis of Structural Complexity Features for Music Genre Recognition</​font></​b>​. Proceedings of the IEEE Congress on Evolutionary Computation ​(CEC), pp. 1-8, <font color=#​996600>​2020</​font></​html>​
  
 <​html><​b><​font color=#​006633>​[30]</​font></​b>​ <i> F. Heerde, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Comparing Fuzzy Rule Based Approaches for Music Genre Classification</​font></​b>​. Proceedings of the 9th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART),​ pp. 35-48, <font color=#​996600>​2020</​font></​html>​ <​html><​b><​font color=#​006633>​[30]</​font></​b>​ <i> F. Heerde, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Comparing Fuzzy Rule Based Approaches for Music Genre Classification</​font></​b>​. Proceedings of the 9th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART),​ pp. 35-48, <font color=#​996600>​2020</​font></​html>​
 
Last modified: 2023-03-21 18:36 by igor.vatolkin
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