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staff:vatolkin:publications [2019-01-12 11:01]
staff:vatolkin:publications [2023-03-21 18:36] (current)
Igor Vatolkin
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 +====== Publication List ======
 +
 ===== Edited Books ===== ===== Edited Books =====
  
-<​html><​b><​font color=#​006633>​[1]</​font></​b>​ <i>C. Weihs, D. Jannach, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Music Data Analysis: Foundations and Applications. </​font></​b>​ CRC Press, <​html><​font color=#​996600>​07.11.2016</​font></​html>​+<​html><​b><​font color=#​006633>​[b1]</​font></​b>​ <i>C. Weihs, D. Jannach, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Music Data Analysis: Foundations and Applications. </​font></​b>​ CRC Press, <​html><​font color=#​996600>​07.11.2016</​font></​html>​
  
-===== Book Chapters ​=====+===== 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>​[6]</​font></​b>​ <i>I. VatolkinC. Weihs</​i>:<​b><​font color=#​0000FF> ​Evaluation</​font></​b>​. ​In: C. WeihsD. Jannach, I. Vatolkin, G. Rudolph ​(Eds.): Music Data Analysis: Foundations and Applications,​ CRC Press, <​html><​font color=#​996600>​2016</​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 Retrieval5(1):1-19, <​html><​font color=#​996600>​2022</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[5]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF> ​Feature Processing</​font></​b>​. ​In: C. WeihsD. Jannach, I. Vatolkin, G. Rudolph ​(Eds.): Music Data Analysis: Foundations and Applications,​ CRC Press, <​html><​font color=#​996600>​2016</​font></​html>​+<​html><​b><​font color=#​006633>​[j7]</​font></​b>​ <i>F. Ostermann, ​I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF> ​Evaluating Creativity in Automatic Reactive Accompaniment of Jazz Improvisation</​font></​b>​. ​Transactions of the International Society for Music Information Retrieval4(1):210-222, <​html><​font color=#​996600>​2021</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[4]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF> ​Feature Selection</​font></​b>​. ​In: C. WeihsD. Jannach, I. Vatolkin, G. Rudolph ​(Eds.): Music Data Analysis: Foundations and Applications,​ CRC Press, <​html><​font color=#​996600>​2016</​font></​html>​+<​html><​b><​font color=#​006633>​[j6]</​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>​. ​Entropy23(11), <​html><​font color=#​996600>​2021</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[3]</​font></​b>​ <i>G. Rötter, ​I. Vatolkin</​i>:<​b><​font color=#​0000FF> ​Emotions</​font></​b>​. ​In: C. WeihsD. JannachI. Vatolkin, G. Rudolph ​(Eds.): Music Data Analysis: Foundations and Applications,​ CRC Press, <​html><​font color=#​996600>​2016</​font></​html>​+<​html><​b><​font color=#​006633>​[j5]</​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>​. ​Archives of Data ScienceSeries A5(1), <​html><​font color=#​996600>​2018</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[2]</​font></​b>​ <​i>​D. ​JannachIVatolkin, and GBonnin</​i>:<​b><​font color=#​0000FF>​ Music Data: beyond the Signal Level</​font></​b>​. ​In: C. WeihsD. JannachI. Vatolkin, G. Rudolph ​(Eds.): Music Data Analysis: Foundations and Applications,​ CRC Press, <​html><​font color=#​996600>​2016</​font></​html>​+<​html><​b><​font color=#​006633>​[j4]</​font></​b>​ <i>A. K. Hassen, H. Janßen, ​D. AssenmacherMPreuss, and IVatolkin</​i>:<​b><​font color=#​0000FF> ​Classifying ​Music Genres Using Image Classification Neural Networks</​font></​b>​. ​Archives of Data ScienceSeries A5(1), <​html><​font color=#​996600>​2018</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[1]</​font></​b>​ <i>NBauerSKrey, U. Ligges, C. Weihs, and IVatolkin</​i>:<​b><​font color=#​0000FF> ​Segmentation</​font></​b>​. ​In: C. WeihsD. Jannach, I. Vatolkin, G. Rudolph ​(Eds.): Music Data Analysis: Foundations and Applications,​ CRC Press, <​html><​font color=#​996600>​2016</​font></​html>​+<​html><​b><​font color=#​006633>​[j3]</​font></​b>​ <i>DStollerIVatolkin, and HMüller</​i>:<​b><​font color=#​0000FF> ​Intuitive and Efficient Computer-Aided Music Rearrangement with Optimised Processing of Audio Transitions</​font></​b>​. ​Journal of New Music Research47(5):416-437, <​html><​font color=#​996600>​2018</​font></​html>​
  
-===== Journal Articles =====+<​html><​b><​font color=#​006633>​[j2]</​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>​[3]</​font></​b>​ <i>DStoller, I. Vatolkin, ​HMüller</​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>​[j1]</​font></​b>​ <i>H. Blume, B. Bischl, M. Botteck, C. Igel, R. Martin, G. Rötter, G. Rudolph, WTheimer, I. Vatolkin, ​and CWeihs</​i>:<​b><​font color=#​0000FF> ​Towards an Automated Dynamic Organization of Huge Music Archives on Mobile Devices</​font></​b>​. ​IEEE Signal Processing Magazine, 28(4):24-39July <​html><​font color=#​996600>​2011</​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>​[1]</​font></​b>​ <i>H. Blume, B. Bischl, M. Botteck, C. Igel, R. Martin, G. Rötter, G. Rudolph, W. Theimer, I. Vatolkin, and C. Weihs</​i>:<​b><​font color=#0000FF> Towards an Automated Dynamic Organization of Huge Music Archives on Mobile Devices</​font></​b>​. IEEE Signal Processing Magazine, 28(4):​24-39,​ July <​html><​font color=#​996600>​2011</​font></​html>​+===== Book Chapters =====
  
 +<​html><​b><​font color=#​006633>​[bc7]</​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>​[bc6]</​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>​
 +
 +<​html><​b><​font color=#​006633>​[bc5]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Feature Processing</​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>​
 +
 +<​html><​b><​font color=#​006633>​[bc4]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Feature Selection</​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>​
 +
 +<​html><​b><​font color=#​006633>​[bc3]</​font></​b>​ <i>G. Rötter, I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Emotions</​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>​
 +
 +<​html><​b><​font color=#​006633>​[bc2]</​font></​b>​ <i>D. Jannach, I. Vatolkin, and G. Bonnin</​i>:<​b><​font color=#​0000FF>​ Music Data: beyond the Signal Level</​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>​
 +
 +<​html><​b><​font color=#​006633>​[bc1]</​font></​b>​ <i>N. Bauer, S. Krey, U. Ligges, C. Weihs, and I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Segmentation</​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>​
  
 ===== Peer-Reviewed Conference Proceedings ===== ===== 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>​[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>​. 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>​. 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>​. 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>​. 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>​ 
 + 
 +<​html><​b><​font color=#​006633>​[c31]</​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>​[c30]</​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>​[c29]</​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  +<​html><​b><​font color=#​006633>​[c28]</​font></​b>​ <i> I. Vatolkin and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Comparison of Audio Features for Recognition of Western and Ethnic  
-Instruments in Polyphonic Mixtures</​font></​b>​. Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), pp. 554-560,<​font color=#​996600>​2018</​font></​html>​+Instruments in Polyphonic Mixtures</​font></​b>​. Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), pp. 554-560, <font color=#​996600>​2018</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[27]</​font></​b>​ <i>F. Scholz, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Singing Voice Detection across Different Music Genres</​font></​b>​. Proceedings of the 2017 AES Conference on Semantic Audio, <font color=#​996600>​2017</​font></​html>​+<​html><​b><​font color=#​006633>​[c27]</​font></​b>​ <i>F. Scholz, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Singing Voice Detection across Different Music Genres</​font></​b>​. Proceedings of the 2017 AES Conference on Semantic Audio, <font color=#​996600>​2017</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[26]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Generalisation Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples</​font></​b>​. Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART),​ pp. 304-320, <font color=#​996600>​2017</​font></​html>​+<​html><​b><​font color=#​006633>​[c26]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Generalisation Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples</​font></​b>​. Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART),​ pp. 304-320, <font color=#​996600>​2017</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[25]</​font></​b>​ <i>F. Ostermann, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos</​font></​b>​. Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART),​ pp. 246-261, <font color=#​996600>​2017</​font></​html>​+<​html><​b><​font color=#​006633>​[c25]</​font></​b>​ <i>F. Ostermann, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos</​font></​b>​. Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART),​ pp. 246-261, <font color=#​996600>​2017</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[24]</​font></​b>​ <i>I. Vatolkin, G. Rudolph, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Evaluation of Album Effect for Feature Selection in Music Genre Recognition</​font></​b>​. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), pp. 169-175, <font color=#​996600>​2015</​font></​html>​+<​html><​b><​font color=#​006633>​[c24]</​font></​b>​ <i>I. Vatolkin, G. Rudolph, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Evaluation of Album Effect for Feature Selection in Music Genre Recognition</​font></​b>​. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), pp. 169-175, <font color=#​996600>​2015</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[23]</​font></​b>​ <i>I. Vatolkin, G. Rudolph, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Interpretability of Music Classification as a Criterion for Evolutionary Multi-Objective Feature Selection</​font></​b>​. Proceedings of the 4th International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART),​ pp. 236-248, <font color=#​996600>​2015</​font></​html>​+<​html><​b><​font color=#​006633>​[c23]</​font></​b>​ <i>I. Vatolkin, G. Rudolph, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Interpretability of Music Classification as a Criterion for Evolutionary Multi-Objective Feature Selection</​font></​b>​. Proceedings of the 4th International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART),​ pp. 236-248, <font color=#​996600>​2015</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[22]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: a Survey and a Case Study (Application to Music Categorisation)</​font></​b>​. Proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization (EMO), pp. 529-543, <font color=#​996600>​2015</​font></​html>​+<​html><​b><​font color=#​006633>​[c22]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: a Survey and a Case Study (Application to Music Categorisation)</​font></​b>​. Proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization (EMO), pp. 529-543, <font color=#​996600>​2015</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[21]</​font></​b>​ <i>I. Vatolkin. G. Bonnin, and D. Jannach</​i>:<​b><​font color=#​0000FF>​ Comparing Audio Features and Playlist Statistics for Music+<​html><​b><​font color=#​006633>​[c21]</​font></​b>​ <i>I. Vatolkin. G. Bonnin, and D. Jannach</​i>:<​b><​font color=#​0000FF>​ Comparing Audio Features and Playlist Statistics for Music
 Classification</​font></​b>​. Proceedings of the 2nd European Conference on Data Analysis (ECDA 2014), pp. 437-447, <font color=#​996600>​2016</​font></​html>​ Classification</​font></​b>​. Proceedings of the 2nd European Conference on Data Analysis (ECDA 2014), pp. 437-447, <font color=#​996600>​2016</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[20]</​font></​b>​ <i>I. Vatolkin and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Interpretable Music Categorisation based on Fuzzy Rules and High-Level Audio Features Features</​font></​b>​. Proceedings of the 1st European Conference on Data Analysis (ECDA 2013), pp. 423-432, <font color=#​996600>​2015</​font></​html>​+<​html><​b><​font color=#​006633>​[c20]</​font></​b>​ <i>I. Vatolkin and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Interpretable Music Categorisation based on Fuzzy Rules and High-Level Audio Features Features</​font></​b>​. Proceedings of the 1st European Conference on Data Analysis (ECDA 2013), pp. 423-432, <font color=#​996600>​2015</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[19]</​font></​b>​ <i>D. Stoller, M. Mauch, I. Vatolkin, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Impact of Frame Size and Instrumentation on Chroma-based Automatic Chord Recognition</​font></​b>​. Proceedings of the 1st European Conference on Data Analysis (ECDA 2013), pp. 411-421, <span style="​color:​red;"><​b>​best paper award</​b></​span>,​ <font color=#​996600>​2015</​font></​html>​+<​html><​b><​font color=#​006633>​[c19]</​font></​b>​ <i>D. Stoller, M. Mauch, I. Vatolkin, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Impact of Frame Size and Instrumentation on Chroma-based Automatic Chord Recognition</​font></​b>​. Proceedings of the 1st European Conference on Data Analysis (ECDA 2013), pp. 411-421, <span style="​color:​red;"><​b>​best paper award</​b></​span>,​ <font color=#​996600>​2015</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[18]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Measuring the Performance of Evolutionary Multi-Objective Feature Selection for Prediction of Musical Genres and Styles</​font></​b>​. Proceedings of the 2nd Workshop Audiosignal- und Sprachverarbeitung (WASP) at INFORMATIK 2013, pp. 3012-3025, <font color=#​996600>​2013</​font></​html>​+<​html><​b><​font color=#​006633>​[c18]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Measuring the Performance of Evolutionary Multi-Objective Feature Selection for Prediction of Musical Genres and Styles</​font></​b>​. Proceedings of the 2nd Workshop Audiosignal- und Sprachverarbeitung (WASP) at INFORMATIK 2013, pp. 3012-3025, <font color=#​996600>​2013</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[17]</​font></​b>​ <i>I. Vatolkin, A. Nagathil, W. Theimer, and R. Martin</​i>:<​b><​font color=#​0000FF>​ Performance of Specific vs. Generic Feature Sets in Polyphonic Music Instrument Recognition</​font></​b>​. Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO), pp. 587-599, <font color=#​996600>​2013</​font></​html>​+<​html><​b><​font color=#​006633>​[c17]</​font></​b>​ <i>I. Vatolkin, A. Nagathil, W. Theimer, and R. Martin</​i>:<​b><​font color=#​0000FF>​ Performance of Specific vs. Generic Feature Sets in Polyphonic Music Instrument Recognition</​font></​b>​. Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO), pp. 587-599, <font color=#​996600>​2013</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[16]</​font></​b>​ <i>I. Vatolkin, G. Rötter, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Music Genre Prediction by Low-Level and High-Level Characteristics</​font></​b>​. ​ Accepted for Proceedings of the 36th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2012</​font></​html>​+<​html><​b><​font color=#​006633>​[c16]</​font></​b>​ <i>I. Vatolkin, G. Rötter, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Music Genre Prediction by Low-Level and High-Level Characteristics</​font></​b>​. ​ Accepted for Proceedings of the 36th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2012</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[15]</​font></​b>​ <i>I. Vatolkin, B. Bischl, G. Rudolph, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Statistical Comparison of Classifiers for Multi-Objective Feature Selection in Instrument Recognition</​font></​b>​. ​ Accepted for Proceedings of the 36th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2012</​font></​html>​+<​html><​b><​font color=#​006633>​[c15]</​font></​b>​ <i>I. Vatolkin, B. Bischl, G. Rudolph, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Statistical Comparison of Classifiers for Multi-Objective Feature Selection in Instrument Recognition</​font></​b>​. ​ Accepted for Proceedings of the 36th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2012</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[14]</​font></​b>​ <i>A. Jordan, D. Scheftelowitsch,​ J. Lahni, J. Hartwecker, M. Kuchem, M. Walter-Huber,​ N. Vortmeier, T. Delbruegger,​ Ü. Güler, I. Vatolkin, and M. Preuss</​i>:<​b><​font color=#​0000FF>​ BeatTheBeat:​ Music-Based Procedural Content Generation In a Mobile Game</​font></​b>​. Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 320-327, <font color=#​996600>​2012</​font></​html>​+<​html><​b><​font color=#​006633>​[c14]</​font></​b>​ <i>A. Jordan, D. Scheftelowitsch,​ J. Lahni, J. Hartwecker, M. Kuchem, M. Walter-Huber,​ N. Vortmeier, T. Delbruegger,​ Ü. Güler, I. Vatolkin, and M. Preuss</​i>:<​b><​font color=#​0000FF>​ BeatTheBeat:​ Music-Based Procedural Content Generation In a Mobile Game</​font></​b>​. Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 320-327, <font color=#​996600>​2012</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[13]</​font></​b>​ <i>G. Rötter, I. Vatolkin, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Computational Prediction of High-Level Descriptors of Music Personal Categories</​font></​b>​. Accepted for Proceedings of the 35th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2011</​font></​html>​+<​html><​b><​font color=#​006633>​[c13]</​font></​b>​ <i>G. Rötter, I. Vatolkin, and C. Weihs</​i>:<​b><​font color=#​0000FF>​ Computational Prediction of High-Level Descriptors of Music Personal Categories</​font></​b>​. Accepted for Proceedings of the 35th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2011</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[12]</​font></​b>​ <i>V. Mattern, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ A Case Study about the Effort to Classify Music Intervals by Chroma and Spectrum Analysis</​font></​b>​. Accepted for Proceedings of the 35th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2011</​font></​html>​+<​html><​b><​font color=#​006633>​[c12]</​font></​b>​ <i>V. Mattern, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ A Case Study about the Effort to Classify Music Intervals by Chroma and Spectrum Analysis</​font></​b>​. Accepted for Proceedings of the 35th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2011</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[11]</​font></​b>​ <i>T. Deinert, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Regression-Based Tempo Recognition from Chroma and Energy Accents for Slow Audio Recordings</​font></​b>​. Proceedings of the AES 42nd International Conference on Semantic Audio (AES), pp. 60-68, <​html><​font color=#​996600>​2011</​font></​html>​+<​html><​b><​font color=#​006633>​[c11]</​font></​b>​ <i>T. Deinert, I. Vatolkin, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Regression-Based Tempo Recognition from Chroma and Energy Accents for Slow Audio Recordings</​font></​b>​. Proceedings of the AES 42nd International Conference on Semantic Audio (AES), pp. 60-68, <​html><​font color=#​996600>​2011</​font></​html>​
   ​   ​
-<​html><​b><​font color=#​006633>​[10]</​font></​b>​ <i>I. Vatolkin, M. Preuß, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Multi-Objective Feature Selection in Music Genre and Style Recognition Tasks</​font></​b>​. Proceedings of the 2011 Genetic and Evolutionary Computation Conference (GECCO), pp. 411-418, <font color=#​996600>​2011</​font></​html>​+<​html><​b><​font color=#​006633>​[c10]</​font></​b>​ <i>I. Vatolkin, M. Preuß, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Multi-Objective Feature Selection in Music Genre and Style Recognition Tasks</​font></​b>​. Proceedings of the 2011 Genetic and Evolutionary Computation Conference (GECCO), pp. 411-418, <font color=#​996600>​2011</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[9]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Multi-Objective Evaluation of Music Classification</​font></​b>​. Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2010</​font></​html>​+<​html><​b><​font color=#​006633>​[c9]</​font></​b>​ <i>I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Multi-Objective Evaluation of Music Classification</​font></​b>​. Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2010</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[8]</​font></​b>​ <i>I. Vatolkin, W. Theimer, and M. Botteck</​i>:<​b><​font color=#​0000FF>​ Partition Based Feature Processing for Improved Music Classification</​font></​b>​. Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2010</​font></​html>​+<​html><​b><​font color=#​006633>​[c8]</​font></​b>​ <i>I. Vatolkin, W. Theimer, and M. Botteck</​i>:<​b><​font color=#​0000FF>​ Partition Based Feature Processing for Improved Music Classification</​font></​b>​. Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2010</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[7]</​font></​b>​ <i>C. Weihs, K. Friedrichs, M. Eichhoff, and I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Software in Music Information Retrieval (MIR)</​font></​b>​. Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2010</​font></​html>​+<​html><​b><​font color=#​006633>​[c7]</​font></​b>​ <i>C. Weihs, K. Friedrichs, M. Eichhoff, and I. Vatolkin</​i>:<​b><​font color=#​0000FF>​ Software in Music Information Retrieval (MIR)</​font></​b>​. Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), <font color=#​996600>​2010</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[6]</​font></​b>​ <i>A. Nagathil, I. Vatolkin, and W. Theimer</​i>:<​b><​font color=#​0000FF>​ Comparison of Partition-Based Audio Features for Music Classification</​font></​b>​. Proceedings the 9th ITG Fachtagung Sprachkommunikation,​ <font color=#​996600>​2010</​font></​html>​+<​html><​b><​font color=#​006633>​[c6]</​font></​b>​ <i>A. Nagathil, I. Vatolkin, and W. Theimer</​i>:<​b><​font color=#​0000FF>​ Comparison of Partition-Based Audio Features for Music Classification</​font></​b>​. Proceedings the 9th ITG Fachtagung Sprachkommunikation,​ <font color=#​996600>​2010</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[5]</​font></​b>​ <i>B. Bischl, I. Vatolkin and M. Preuß</​i>:<​b><​font color=#​0000FF>​ Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation</​font></​b>​. Proceedings of the 11th International Conference on Parallel Problem Solving From Nature (PPSN), pp. 314-323, <font color=#​996600>​2010</​font></​html>​+<​html><​b><​font color=#​006633>​[c5]</​font></​b>​ <i>B. Bischl, I. Vatolkin and M. Preuß</​i>:<​b><​font color=#​0000FF>​ Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation</​font></​b>​. Proceedings of the 11th International Conference on Parallel Problem Solving From Nature (PPSN), pp. 314-323, <font color=#​996600>​2010</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[4]</​font></​b>​ <i>I. Vatolkin, W. Theimer, and M.Botteck</​i>:<​b><​font color=#​0000FF>​ AMUSE (Advanced MUSic Explorer) – A Multitool Framework for Music Data Analysis</​font></​b>​. Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR), pp. 33-38, <font color=#​996600>​2010</​font></​html>​+<​html><​b><​font color=#​006633>​[c4]</​font></​b>​ <i>I. Vatolkin, W. Theimer, and M.Botteck</​i>:<​b><​font color=#​0000FF>​ AMUSE (Advanced MUSic Explorer) – A Multitool Framework for Music Data Analysis</​font></​b>​. Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR), pp. 33-38, <font color=#​996600>​2010</​font></​html>​
   ​   ​
-<​html><​b><​font color=#​006633>​[3]</​font></​b>​ <i>I. Vatolkin, W. Theimer, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Design and Comparison of Different Evolution Strategies for Feature Selection and Consolidation in Music Classification</​font></​b>​. Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC), pp. 174-181, <font color=#​996600>​2009</​font></​html>​+<​html><​b><​font color=#​006633>​[c3]</​font></​b>​ <i>I. Vatolkin, W. Theimer, and G. Rudolph</​i>:<​b><​font color=#​0000FF>​ Design and Comparison of Different Evolution Strategies for Feature Selection and Consolidation in Music Classification</​font></​b>​. Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC), pp. 174-181, <font color=#​996600>​2009</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[2]</​font></​b>​ <i>I. Vatolkin and W. Theimer</​i>:<​b><​font color=#​0000FF>​ Optimization of Feature Processing Chain in Music Classification by Evolutionary Strategies</​font></​b>​. Proceedings of the 10th International Conference on Parallel Problem Solving from Nature (PPSN), pp. 1150-1159, <font color=#​996600>​2008</​font></​html>​+<​html><​b><​font color=#​006633>​[c2]</​font></​b>​ <i>I. Vatolkin and W. Theimer</​i>:<​b><​font color=#​0000FF>​ Optimization of Feature Processing Chain in Music Classification by Evolutionary Strategies</​font></​b>​. Proceedings of the 10th International Conference on Parallel Problem Solving from Nature (PPSN), pp. 1150-1159, <font color=#​996600>​2008</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[1]</​font></​b>​ <i>W. Theimer, I. Vatolkin, M. Botteck, and M. Buchmann</​i>:<​b><​font color=#​0000FF>​ Content-Based Similarity Search and Visualization for Personal Music Categories</​font></​b>​. Proceedings of the 6th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 9-16, <font color=#​996600>​2008</​font></​html>​+<​html><​b><​font color=#​006633>​[c1]</​font></​b>​ <i>W. Theimer, I. Vatolkin, M. Botteck, and M. Buchmann</​i>:<​b><​font color=#​0000FF>​ Content-Based Similarity Search and Visualization for Personal Music Categories</​font></​b>​. Proceedings of the 6th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 9-16, <font color=#​996600>​2008</​font></​html>​
  
 ===== PhD ===== ===== PhD =====
  
-<​html><​b><​font color=#​006633>​[1]</​font></​b>​ <i>I. Vatolkin</​i>:​ <​b><​a href="​https://​eldorado.tu-dortmund.de/​bitstream/​2003/​30402/​1/​Dissertation.pdf"><​font color=#​0000FF>​Improving Supervised Music Classification by Means of Multi-Objective Evolutionary Feature Selection</​font></​a></​b>​. Department of Computer Science, TU Dortmund <font color=#​996600>​2013</​font></​html>​+<​html><​b><​font color=#​006633>​[p1]</​font></​b>​ <i>I. Vatolkin</​i>:​ <​b><​a href="​https://​eldorado.tu-dortmund.de/​bitstream/​2003/​30402/​1/​Dissertation.pdf"><​font color=#​0000FF>​Improving Supervised Music Classification by Means of Multi-Objective Evolutionary Feature Selection</​font></​a></​b>​. Department of Computer Science, TU Dortmund <font color=#​996600>​2013</​font></​html>​
  
 ===== Technical Reports ===== ===== Technical Reports =====
  
-<​html><​b><​font color=#​006633>​[3]</​font></​b>​ <i>I. Vatolkin, M. Preuß, and G. Rudolph</​i>:​ <​b><​a href="​http://​ls11-www.cs.tu-dortmund.de/​_media/​techreports/​tr13-01.pdf"><​font color=#​0000FF>​Training Set Reduction Based on 2-Gram Feature Statistics for Music Genre Recognition</​font></​a></​b>​. 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), <font color=#​996600>​2013</​font></​html>​+<​html><​b><​font color=#​006633>​[tr3]</​font></​b>​ <i>I. Vatolkin, M. Preuß, and G. Rudolph</​i>:​ <​b><​a href="​http://​ls11-www.cs.tu-dortmund.de/​_media/​techreports/​tr13-01.pdf"><​font color=#​0000FF>​Training Set Reduction Based on 2-Gram Feature Statistics for Music Genre Recognition</​font></​a></​b>​. 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), <font color=#​996600>​2013</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[2]</​font></​b>​ <i>W. Theimer, I. Vatolkin, and A. Eronen</​i>:​ <​b><​a href="/​people/​vatol/​publications/​PDFs/​TR08_2_001.pdf"><​font color=#​0000FF>​Definitions of Audio Features for Music Content Description</​font></​a></​b>​. Technical Report TR08-2-001, Chair of Algorithm Engineering,​ University of Dortmund, <font color=#​996600>​2008</​font></​html>​+<​html><​b><​font color=#​006633>​[tr2]</​font></​b>​ <i>W. Theimer, I. Vatolkin, and A. Eronen</​i>:​ <​b><​a href="/​people/​vatol/​publications/​PDFs/​TR08_2_001.pdf"><​font color=#​0000FF>​Definitions of Audio Features for Music Content Description</​font></​a></​b>​. Technical Report TR08-2-001, Chair of Algorithm Engineering,​ University of Dortmund, <font color=#​996600>​2008</​font></​html>​
  
-<​html><​b><​font color=#​006633>​[1]</​font></​b>​ <i>I. Vatolkin and W. Theimer</​i>:​ <​b><​a href="/​people/​vatol/​publications/​PDFs/​NRC-TR-2007-012.pdf"><​font color=#​0000FF>​Introduction to Methods for Music Classification Based on Audio Data</​font></​a></​b>​. Nokia Research Center Technical Report NRC-TR-2007-012,​ <font color=#​996600>​2007</​font></​html>​+<​html><​b><​font color=#​006633>​[tr1]</​font></​b>​ <i>I. Vatolkin and W. Theimer</​i>:​ <​b><​a href="/​people/​vatol/​publications/​PDFs/​NRC-TR-2007-012.pdf"><​font color=#​0000FF>​Introduction to Methods for Music Classification Based on Audio Data</​font></​a></​b>​. Nokia Research Center Technical Report NRC-TR-2007-012,​ <font color=#​996600>​2007</​font></​html>​
  
  
 
Last modified: 2019-01-12 11:01 (external edit)
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