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| ===== Journal Articles ===== | ===== Journal Articles ===== | ||
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| + | <html><b><font color=#006633>[6]</font></b> <i>B. Wilkes, I. Vatolkin, and H. Müller</i>:<b><font color=#0000FF> Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition </font></b>. Entropy, 23(11) <html><font color=#996600>2021</font></html> | ||
| <html><b><font color=#006633>[5]</font></b> <i>I. Vatolkin</i>:<b><font color=#0000FF> Robustness of Features and Classification Models on Degraded Data Sets in Music Classification </font></b>. Accepted for Archives of Data Science, Series A, <html><font color=#996600>2018</font></html> | <html><b><font color=#006633>[5]</font></b> <i>I. Vatolkin</i>:<b><font color=#0000FF> Robustness of Features and Classification Models on Degraded Data Sets in Music Classification </font></b>. Accepted for Archives of Data Science, Series A, <html><font color=#996600>2018</font></html> | ||
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| <html><b><font color=#006633>[4]</font></b> <i>A. K. Hassen, H. Janßen, D. Assenmacher, M. Preuss, and I. Vatolkin</i>:<b><font color=#0000FF> Classifying Music Genres Using Image Classification Neural Networks</font></b>. Archives of Data Science, Series A, 5(1):1-18, <html><font color=#996600>2018</font></html> | <html><b><font color=#006633>[4]</font></b> <i>A. K. Hassen, H. Janßen, D. Assenmacher, M. Preuss, and I. Vatolkin</i>:<b><font color=#0000FF> Classifying Music Genres Using Image Classification Neural Networks</font></b>. Archives of Data Science, Series A, 5(1):1-18, <html><font color=#996600>2018</font></html> | ||
| - | <html><b><font color=#006633>[3]</font></b> <i>D. Stoller, I. Vatolkin, H. Müller</i>:<b><font color=#0000FF> Intuitive and Efficient Computer-Aided Music Rearrangement with Optimised Processing of Audio Transitions</font></b>. Journal of New Music Research, 47(5):416-437, <html><font color=#996600>2018</font></html> | + | <html><b><font color=#006633>[3]</font></b> <i>D. Stoller, I. Vatolkin, and H. Müller</i>:<b><font color=#0000FF> Intuitive and Efficient Computer-Aided Music Rearrangement with Optimised Processing of Audio Transitions</font></b>. Journal of New Music Research, 47(5):416-437, <html><font color=#996600>2018</font></html> |
| <html><b><font color=#006633>[2]</font></b> <i>I. Vatolkin, M. Preuß, G. Rudolph, M. Eichhoff, and C. Weihs</i>:<b><font color=#0000FF> Multi-Objective Evolutionary Feature Selection for Instrument Recognition in Polyphonic Audio Mixtures</font></b>. Soft Computing – A Fusion of Foundations, Methodologies and Applications, 16(12):2027-2047, <html><font color=#996600>2012</font></html> | <html><b><font color=#006633>[2]</font></b> <i>I. Vatolkin, M. Preuß, G. Rudolph, M. Eichhoff, and C. Weihs</i>:<b><font color=#0000FF> Multi-Objective Evolutionary Feature Selection for Instrument Recognition in Polyphonic Audio Mixtures</font></b>. Soft Computing – A Fusion of Foundations, Methodologies and Applications, 16(12):2027-2047, <html><font color=#996600>2012</font></html> | ||
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| ===== Peer-Reviewed Conference Proceedings ===== | ===== Peer-Reviewed Conference Proceedings ===== | ||
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| + | <html><b><font color=#006633>[37]</font></b> <i> I. Vatolkin</i>:<b><font color=#0000FF> Improving Interpretable Genre Recognition with Audio Feature Statistics Based on Zygonic Theory</font></b>. Proceedings of the 2nd Nordic Sound and Computing Conference (NordicSMC), <font color=#996600>2021</font></html> | ||
| <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>[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> | ||