[b1] C. Weihs, D. Jannach, I. Vatolkin, and G. Rudolph: Music Data Analysis: Foundations and Applications. CRC Press, 07.11.2016

[j9] F. Ostermann, I. Vatolkin, and M. Ebeling: AAM: A Dataset of Artificial Audio Multitracks for Diverse Music Information Retrieval Tasks. Accepted for EURASIP Journal on Audio, Speech, and Music Processing, 2023

[j8] I. Vatolkin and C. McKay: Multi-Objective Investigation of Six Feature Source Types for Multi-Modal Music Classification. Transactions of the International Society for Music Information Retrieval, 5(1):1-19, 2022

[j7] F. Ostermann, I. Vatolkin, and G. Rudolph: Evaluating Creativity in Automatic Reactive Accompaniment of Jazz Improvisation. Transactions of the International Society for Music Information Retrieval, 4(1):210-222, 2021

[j6] B. Wilkes, I. Vatolkin, and H. Müller: Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition. Entropy, 23(11), 2021

[j5] I. Vatolkin: Robustness of Features and Classification Models on Degraded Data Sets in Music Classification. Archives of Data Science, Series A, 5(1), 2018

[j4] A. K. Hassen, H. Janßen, D. Assenmacher, M. Preuss, and I. Vatolkin: Classifying Music Genres Using Image Classification Neural Networks. Archives of Data Science, Series A, 5(1), 2018

[j3] D. Stoller, I. Vatolkin, and H. Müller: Intuitive and Efficient Computer-Aided Music Rearrangement with Optimised Processing of Audio Transitions. Journal of New Music Research, 47(5):416-437, 2018

[j2] I. Vatolkin, M. Preuß, G. Rudolph, M. Eichhoff, and C. Weihs: Multi-Objective Evolutionary Feature Selection for Instrument Recognition in Polyphonic Audio Mixtures. Soft Computing – A Fusion of Foundations, Methodologies and Applications, 16(12):2027-2047, 2012

[j1] H. Blume, B. Bischl, M. Botteck, C. Igel, R. Martin, G. Rötter, G. Rudolph, W. Theimer, I. Vatolkin, and C. Weihs: Towards an Automated Dynamic Organization of Huge Music Archives on Mobile Devices. IEEE Signal Processing Magazine, 28(4):24-39, July 2011

[bc7] I. Vatolkin, A. Nagathil: Evaluation of Audio Feature Groups for the Prediction of Arousal and Valence in Music. 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, 2019

[bc6] I. Vatolkin, C. Weihs: Evaluation. In: C. Weihs, D. Jannach, I. Vatolkin, G. Rudolph (Eds.): Music Data Analysis: Foundations and Applications, CRC Press, 2016

[bc5] I. Vatolkin: Feature Processing. In: C. Weihs, D. Jannach, I. Vatolkin, G. Rudolph (Eds.): Music Data Analysis: Foundations and Applications, CRC Press, 2016

[bc4] I. Vatolkin: Feature Selection. In: C. Weihs, D. Jannach, I. Vatolkin, G. Rudolph (Eds.): Music Data Analysis: Foundations and Applications, CRC Press, 2016

[bc3] G. Rötter, I. Vatolkin: Emotions. In: C. Weihs, D. Jannach, I. Vatolkin, G. Rudolph (Eds.): Music Data Analysis: Foundations and Applications, CRC Press, 2016

[bc2] D. Jannach, I. Vatolkin, and G. Bonnin: Music Data: beyond the Signal Level. In: C. Weihs, D. Jannach, I. Vatolkin, G. Rudolph (Eds.): Music Data Analysis: Foundations and Applications, CRC Press, 2016

[bc1] N. Bauer, S. Krey, U. Ligges, C. Weihs, and I. Vatolkin: Segmentation. In: C. Weihs, D. Jannach, I. Vatolkin, G. Rudolph (Eds.): Music Data Analysis: Foundations and Applications, CRC Press, 2016

[c43] L. Fricke, I. Vatolkin, and F. Ostermann: Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio. Accepted for Proceedings of the 12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), 2023

[c42] I. Vatolkin, M. Gotham, N. Nápoles López, and F. Ostermann: Musical Genre Recognition based on Deep Descriptors of Harmony, Instrumentation, and Segments. Accepted for Proceedings of the 12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), 2023

[c41] L. Fricke, J. Kuzmic, and I. Vatolkin: Suppression of Background Noise in Speech Signals with Artificial Neural Networks, Exemplarily Applied to Keyboard Sounds. Proceedings of the 14th International Conference on Neural Computation Theory and Applications (NCTA), pp. 367-374, 2022

[c40] I. Vatolkin and C. McKay: Stability of Symbolic Feature Group Importance in the Context of Multi-Modal Music Classification. Proceedings of the The 23rd International Society for Music Information Retrieval Conference (ISMIR), pp.469-476, 2022

[c39] F. Ostermann, I. Vatolkin, and G. Rudolph: Artificial Music Producer: Filtering Music Compositions by Artificial Taste. Proceedings of the 3rd Conference on AI Music Creativity (AIMC), 2022

[c38] I. Vatolkin: Identification of the Most Relevant Zygonic Statistics and Semantic Audio Features for Genre Recognition. Proceedings of the International Computer Music Conference (ICMC), 2022

[c37] I. Vatolkin: Improving Interpretable Genre Recognition with Audio Feature Statistics Based on Zygonic Theory. Proceedings of the 2nd Nordic Sound and Computing Conference (NordicSMC), 2021

[c36] I. Vatolkin, P. Ginsel, and G. Rudolph: Advancements in the Music Information Retrieval Framework AMUSE over the Last Decade. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 2383-2389, 2021

[c35] I. Vatolkin, F. Ostermann, and M. Müller: An Evolutionary Multi-Objective Feature Selection Approach for Detecting Music Segment Boundaries of Specific Types. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1061-1069, 2021

[c34] I. Vatolkin, B. Adrian, and J. Kuzmic: A Fusion of Deep and Shallow Learning to Predict Genres Based on Instrument and Timbre Features. Proceedings of the 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 313-326, 2021

[c33] I. Vatolkin, M. Koch, and M. Müller: A Multi-Objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation. Proceedings of the 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 327-343, 2021

[c32] I. Vatolkin: Evolutionary Approximation of Instrumental Texture in Polyphonic Audio Recordings. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, 2020

[c31] P. Ginsel, I. Vatolkin, and G. Rudolph: Analysis of Structural Complexity Features for Music Genre Recognition. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, 2020

[c30] F. Heerde, I. Vatolkin, and G. Rudolph: Comparing Fuzzy Rule Based Approaches for Music Genre Classification. Proceedings of the 9th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 35-48, 2020

[c29] I. Vatolkin and D. Stoller: Evolutionary Multi-Objective Training Set Selection of Data Instances and Augmentations for Vocal Detection. Proceedings of the 8th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 201-216, 2019

[c28] I. Vatolkin and G. Rudolph: Comparison of Audio Features for Recognition of Western and Ethnic Instruments in Polyphonic Mixtures. Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), pp. 554-560, 2018

[c27] F. Scholz, I. Vatolkin, and G. Rudolph: Singing Voice Detection across Different Music Genres. Proceedings of the 2017 AES Conference on Semantic Audio, 2017

[c26] I. Vatolkin: Generalisation Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples. Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 304-320, 2017

[c25] F. Ostermann, I. Vatolkin, and G. Rudolph: Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos. Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 246-261, 2017

[c24] I. Vatolkin, G. Rudolph, and C. Weihs: Evaluation of Album Effect for Feature Selection in Music Genre Recognition. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), pp. 169-175, 2015

[c23] I. Vatolkin, G. Rudolph, and C. Weihs: Interpretability of Music Classification as a Criterion for Evolutionary Multi-Objective Feature Selection. Proceedings of the 4th International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART), pp. 236-248, 2015

[c22] I. Vatolkin: Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: a Survey and a Case Study (Application to Music Categorisation). Proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization (EMO), pp. 529-543, 2015

[c21] I. Vatolkin. G. Bonnin, and D. Jannach: Comparing Audio Features and Playlist Statistics for Music Classification. Proceedings of the 2nd European Conference on Data Analysis (ECDA 2014), pp. 437-447, 2016

[c20] I. Vatolkin and G. Rudolph: Interpretable Music Categorisation based on Fuzzy Rules and High-Level Audio Features Features. Proceedings of the 1st European Conference on Data Analysis (ECDA 2013), pp. 423-432, 2015

[c19] D. Stoller, M. Mauch, I. Vatolkin, and C. Weihs: Impact of Frame Size and Instrumentation on Chroma-based Automatic Chord Recognition. Proceedings of the 1st European Conference on Data Analysis (ECDA 2013), pp. 411-421, best paper award, 2015

[c18] I. Vatolkin: Measuring the Performance of Evolutionary Multi-Objective Feature Selection for Prediction of Musical Genres and Styles. Proceedings of the 2nd Workshop Audiosignal- und Sprachverarbeitung (WASP) at INFORMATIK 2013, pp. 3012-3025, 2013

[c17] I. Vatolkin, A. Nagathil, W. Theimer, and R. Martin: Performance of Specific vs. Generic Feature Sets in Polyphonic Music Instrument Recognition. Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO), pp. 587-599, 2013

[c16] I. Vatolkin, G. Rötter, and C. Weihs: Music Genre Prediction by Low-Level and High-Level Characteristics. Accepted for Proceedings of the 36th Annual Conference of the German Classification Society (GfKl), 2012

[c15] I. Vatolkin, B. Bischl, G. Rudolph, and C. Weihs: Statistical Comparison of Classifiers for Multi-Objective Feature Selection in Instrument Recognition. Accepted for Proceedings of the 36th Annual Conference of the German Classification Society (GfKl), 2012

[c14] A. Jordan, D. Scheftelowitsch, J. Lahni, J. Hartwecker, M. Kuchem, M. Walter-Huber, N. Vortmeier, T. Delbruegger, Ü. Güler, I. Vatolkin, and M. Preuss: BeatTheBeat: Music-Based Procedural Content Generation In a Mobile Game. Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 320-327, 2012

[c13] G. Rötter, I. Vatolkin, and C. Weihs: Computational Prediction of High-Level Descriptors of Music Personal Categories. Accepted for Proceedings of the 35th Annual Conference of the German Classification Society (GfKl), 2011

[c12] V. Mattern, I. Vatolkin, and G. Rudolph: A Case Study about the Effort to Classify Music Intervals by Chroma and Spectrum Analysis. Accepted for Proceedings of the 35th Annual Conference of the German Classification Society (GfKl), 2011

[c11] T. Deinert, I. Vatolkin, and G. Rudolph: Regression-Based Tempo Recognition from Chroma and Energy Accents for Slow Audio Recordings. Proceedings of the AES 42nd International Conference on Semantic Audio (AES), pp. 60-68, 2011

[c10] I. Vatolkin, M. Preuß, and G. Rudolph: Multi-Objective Feature Selection in Music Genre and Style Recognition Tasks. Proceedings of the 2011 Genetic and Evolutionary Computation Conference (GECCO), pp. 411-418, 2011

[c9] I. Vatolkin: Multi-Objective Evaluation of Music Classification. Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), 2010

[c8] I. Vatolkin, W. Theimer, and M. Botteck: Partition Based Feature Processing for Improved Music Classification. Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), 2010

[c7] C. Weihs, K. Friedrichs, M. Eichhoff, and I. Vatolkin: Software in Music Information Retrieval (MIR). Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), 2010

[c6] A. Nagathil, I. Vatolkin, and W. Theimer: Comparison of Partition-Based Audio Features for Music Classification. Proceedings the 9th ITG Fachtagung Sprachkommunikation, 2010

[c5] B. Bischl, I. Vatolkin and M. Preuß: Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation. Proceedings of the 11th International Conference on Parallel Problem Solving From Nature (PPSN), pp. 314-323, 2010

[c4] I. Vatolkin, W. Theimer, and M.Botteck: AMUSE (Advanced MUSic Explorer) – A Multitool Framework for Music Data Analysis. Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR), pp. 33-38, 2010

[c3] I. Vatolkin, W. Theimer, and G. Rudolph: Design and Comparison of Different Evolution Strategies for Feature Selection and Consolidation in Music Classification. Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC), pp. 174-181, 2009

[c2] I. Vatolkin and W. Theimer: Optimization of Feature Processing Chain in Music Classification by Evolutionary Strategies. Proceedings of the 10th International Conference on Parallel Problem Solving from Nature (PPSN), pp. 1150-1159, 2008

[c1] W. Theimer, I. Vatolkin, M. Botteck, and M. Buchmann: Content-Based Similarity Search and Visualization for Personal Music Categories. Proceedings of the 6th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 9-16, 2008

[p1] I. Vatolkin: Improving Supervised Music Classification by Means of Multi-Objective Evolutionary Feature Selection. Department of Computer Science, TU Dortmund 2013

[tr3] I. Vatolkin, M. Preuß, and G. Rudolph: Training Set Reduction Based on 2-Gram Feature Statistics for Music Genre Recognition. Technical Report TR13-2-001, Chair of Algorithm Engineering, TU Dortmund, presented at the 2012 Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), 2013

[tr2] W. Theimer, I. Vatolkin, and A. Eronen: Definitions of Audio Features for Music Content Description. Technical Report TR08-2-001, Chair of Algorithm Engineering, University of Dortmund, 2008

[tr1] I. Vatolkin and W. Theimer: Introduction to Methods for Music Classification Based on Audio Data. Nokia Research Center Technical Report NRC-TR-2007-012, 2007