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staff:vatolkin:publications [2021-03-28 17:32] igor.vatolkin |
staff:vatolkin:publications [2021-04-25 20:54] igor.vatolkin |
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| ===== Peer-Reviewed Conference Proceedings ===== | ===== Peer-Reviewed Conference Proceedings ===== | ||
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| + | <html><b><font color=#006633>[36]</font></b> <i> I. Vatolkin, P. Ginsel, and G. Rudolph</i>:<b><font color=#0000FF> Advancements in the Music Information Retrieval Framework AMUSE over the Last Decade</font></b>. Accepted for Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), <font color=#996600>2021</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>[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>[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>[34]</font></b> <i> I. Vatolkin, B. Adrian, and 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>[33]</font></b> <i> I. Vatolkin, M. Koch, and 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>2021</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>[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> | ||