Differences

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

--- staff:vatolkin:publications [2022-02-01 16:01]
igor.vatolkin [Journal Articles]
+++ staff:vatolkin:publications [2023-03-21 18:36]
igor.vatolkin
@@ Line 6: / Line 6: @@
 ===== Journal Articles =====
+<html><b><font color=#006633>[j9]</font></b> <i>F. Ostermann, I. Vatolkin, and M. Ebeling</i>:<b><font color=#0000FF> AAM: A Dataset of Artificial Audio Multitracks for Diverse Music Information Retrieval Tasks</font></b>. Accepted for EURASIP Journal on Audio, Speech, and Music Processing, <html><font color=#996600>2023</font></html>
 <html><b><font color=#006633>[j8]</font></b> <i>I. Vatolkin and C. McKay</i>:<b><font color=#0000FF> Multi-Objective Investigation of Six Feature Source Types for Multi-Modal Music Classification</font></b>. Transactions of the International Society for Music Information Retrieval, 5(1):1-19, <html><font color=#996600>2022</font></html>
@@ Line 40: / Line 42: @@
 ===== Peer-Reviewed Conference Proceedings =====
+<html><b><font color=#006633>[c43]</font></b> <i> L. Fricke, I. Vatolkin, and F. Ostermann</i>:<b><font color=#0000FF> Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio</font></b>. Accepted for Proceedings of the 12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), <font color=#996600>2023</font></html>
+<html><b><font color=#006633>[c42]</font></b> <i> I. Vatolkin, M. Gotham, N. Nápoles López, and F. Ostermann</i>:<b><font color=#0000FF> Musical Genre Recognition based on Deep Descriptors of Harmony, Instrumentation, and Segments</font></b>. Accepted for Proceedings of the 12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), <font color=#996600>2023</font></html>
+<html><b><font color=#006633>[c41]</font></b> <i> L. Fricke, J. Kuzmic, and I. Vatolkin</i>:<b><font color=#0000FF> Suppression of Background Noise in Speech Signals with Artificial Neural Networks, Exemplarily Applied to Keyboard Sounds</font></b>. Proceedings of the 14th International Conference on Neural Computation Theory and Applications (NCTA), pp. 367-374, <font color=#996600>2022</font></html>
+<html><b><font color=#006633>[c40]</font></b> <i> I. Vatolkin and C. McKay</i>:<b><font color=#0000FF> Stability of Symbolic Feature Group Importance in the Context of Multi-Modal Music Classification</font></b>. Proceedings of the The 23rd International Society for Music Information Retrieval Conference (ISMIR), pp.469-476, <font color=#996600>2022</font></html>
+<html><b><font color=#006633>[c39]</font></b> <i> F. Ostermann, I. Vatolkin, and G. Rudolph</i>:<b><font color=#0000FF> Artificial Music Producer: Filtering Music Compositions by Artificial Taste</font></b>. Proceedings of the 3rd Conference on AI Music Creativity (AIMC), <font color=#996600>2022</font></html>
+<html><b><font color=#006633>[c38]</font></b> <i> I. Vatolkin</i>:<b><font color=#0000FF> Identification of the Most Relevant Zygonic Statistics and Semantic Audio Features for Genre Recognition</font></b>. Proceedings of the International Computer Music Conference (ICMC), <font color=#996600>2022</font></html>
 <html><b><font color=#006633>[c37]</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>[c36]</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), pp. 2383-2389, <font color=#996600>2021</font></html>
+<html><b><font color=#006633>[c36]</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>. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 2383-2389, <font color=#996600>2021</font></html>
-<html><b><font color=#006633>[c35]</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), pp. 1061-1069, <font color=#996600>2021</font></html>
+<html><b><font color=#006633>[c35]</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>. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1061-1069, <font color=#996600>2021</font></html>
-<html><b><font color=#006633>[c34]</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), pp. 313-326, <font color=#996600>2021</font></html>
+<html><b><font color=#006633>[c34]</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>. Proceedings of the 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 313-326, <font color=#996600>2021</font></html>
-<html><b><font color=#006633>[c33]</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), pp. 327-343, <font color=#996600>2021</font></html>
+<html><b><font color=#006633>[c33]</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>. Proceedings of the 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 327-343, <font color=#996600>2021</font></html>
 <html><b><font color=#006633>[c32]</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>

Chair 11

Teaching

Research Groups

Differences