Differences

This shows you the differences between two versions of the page.

--- staff:vatolkin:publications [2019-01-12 11:18]
+++ staff:vatolkin:publications [2021-04-25 20:54]
igor.vatolkin
@@ Line 6: / Line 6: @@
 ===== Book Chapters =====
+<html><b><font color=#006633>[7]</font></b> <i>I. Vatolkin, A. Nagathil</i>:<b><font color=#0000FF> Evaluation of Audio Feature Groups for the Prediction of Arousal and Valence in Music</font></b>. In: N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, M. Vichi (Eds.) (Eds.): Applications in Statistical Computing: From Music Data Analysis to Industrial Quality Improvement, Springer, <html><font color=#996600>2019</font></html>
 <html><b><font color=#006633>[6]</font></b> <i>I. Vatolkin, C. Weihs</i>:<b><font color=#0000FF> Evaluation</font></b>. In: C. Weihs, D. Jannach, I. Vatolkin, G. Rudolph (Eds.): Music Data Analysis: Foundations and Applications, CRC Press, <html><font color=#996600>2016</font></html>
@@ Line 21: / Line 23: @@
 ===== Journal Articles =====
-<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>. Accepted for Journal of New Music Research, <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>
+<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>[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>
@@ Line 30: / Line 36: @@
 ===== Peer-Reviewed Conference Proceedings =====
-<html><b><font color=#006633>[29]</font></b> <i> I. Vatolkin and D. Stoller</i>:<b><font color=#0000FF> Evolutionary Multi-Objective Training Set Selection of Data Instances and Augmentations for Vocal Detection</font></b>. Accepted for Proceedings of the 8th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART), <font color=#996600>2019</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>[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, 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, 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>[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>[29]</font></b> <i> I. Vatolkin and D. Stoller</i>:<b><font color=#0000FF> Evolutionary Multi-Objective Training Set Selection of Data Instances and Augmentations for Vocal Detection</font></b>. Proceedings of the 8th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 201-216, <font color=#996600>2019</font></html>
 <html><b><font color=#006633>[28]</font></b> <i> I. Vatolkin and G. Rudolph</i>:<b><font color=#0000FF> Comparison of Audio Features for Recognition of Western and Ethnic

Chair 11

Teaching

Research Groups

Differences