Igor Vatolkin

Igor Vatolkin

Technische Universität Dortmund

H-index: 11

Europe-Germany

About Igor Vatolkin

Igor Vatolkin, With an exceptional h-index of 11 and a recent h-index of 7 (since 2020), a distinguished researcher at Technische Universität Dortmund, specializes in the field of Computational Intelligence in Music Data Analysis.

His recent articles reflect a diverse array of research interests and contributions to the field:

Weighted Initialisation of Evolutionary Instrument and Pitch Detection in Polyphonic Music

Adaptation and Optimization of AugmentedNet for Roman Numeral Analysis Applied to Audio Signals

Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio

AAM: a dataset of Artificial Audio Multitracks for diverse music information retrieval tasks

Musical Genre Recognition Based on Deep Descriptors of Harmony, Instrumentation, and Segments

Stability of Symbolic Feature Group Importance in the Context of Multi-Modal Music Classification.

Multi-Objective Investigation of Six Feature Source Types for Multi-Modal Music Classification.

Suppression of Background Noise in Speech Signals with Artificial Neural Networks, Exemplarily Applied to Keyboard Sounds.

Igor Vatolkin Information

University

Position

Postdoc

Citations(all)

453

Citations(since 2020)

201

Cited By

315

hIndex(all)

11

hIndex(since 2020)

7

i10Index(all)

13

i10Index(since 2020)

4

Email

University Profile Page

Technische Universität Dortmund

Google Scholar

View Google Scholar Profile

Igor Vatolkin Skills & Research Interests

Computational Intelligence in Music Data Analysis

Top articles of Igor Vatolkin

Title

Journal

Author(s)

Publication Date

Weighted Initialisation of Evolutionary Instrument and Pitch Detection in Polyphonic Music

Justin Dettmer

Igor Vatolkin

Tobias Glasmachers

2024/3/29

Adaptation and Optimization of AugmentedNet for Roman Numeral Analysis Applied to Audio Signals

Leonard Fricke

Mark Gotham

Fabian Ostermann

Igor Vatolkin

2024/3/29

Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio

Leonard Fricke

Igor Vatolkin

Fabian Ostermann

2023/4/1

AAM: a dataset of Artificial Audio Multitracks for diverse music information retrieval tasks

EURASIP Journal on Audio, Speech, and Music Processing

Fabian Ostermann

Igor Vatolkin

Martin Ebeling

2023/3/23

Musical Genre Recognition Based on Deep Descriptors of Harmony, Instrumentation, and Segments

Igor Vatolkin

Mark Gotham

Néstor Nápoles López

Fabian Ostermann

2023/4/1

Stability of Symbolic Feature Group Importance in the Context of Multi-Modal Music Classification.

Igor Vatolkin

Cory McKay

2022/12/4

Multi-Objective Investigation of Six Feature Source Types for Multi-Modal Music Classification.

Transactions of the International Society for Music Information Retrieval

Igor Vatolkin

Cory McKay

2022/1/24

Suppression of Background Noise in Speech Signals with Artificial Neural Networks, Exemplarily Applied to Keyboard Sounds.

Leonard Fricke

Jurij Kuzmic

Igor Vatolkin

2022

Evaluating Creativity in Automatic Reactive Accompaniment of Jazz Improvisation.

Transactions of the International Society for Music Information Retrieval

Fabian Ostermann

Igor Vatolkin

Günter Rudolph

2021/11/30

IMPROVING INTERPRETABLE GENRE RECOGNITION WITH AUDIO FEATURE STATISTICS BASED ON ZYGONIC THEORY

Igor VATOLKIN

2021

Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition

Entropy

Ben Wilkes

Igor Vatolkin

Heinrich Müller

2021/11/12

Advancements in the music information retrieval framework amuse over the last decade

Igor Vatolkin

Philipp Ginsel

Günter Rudolph

2021/7/11

An evolutionary multi-objective feature selection approach for detecting music segment boundaries of specific types

Igor Vatolkin

Fabian Ostermann

Meinard Müller

2021/6/26

A Multi-objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation

Igor Vatolkin

Marcel Koch

Meinard Müller

2021/4/2

A fusion of deep and shallow learning to predict genres based on instrument and timbre features

Igor Vatolkin

Benedikt Adrian

Jurij Kuzmic

2021/4/2

Analysis of structural complexity features for music genre recognition

Philipp Ginsel

Igor Vatolkin

Güter Rudolph

2020/7/19

Evolutionary approximation of instrumental texture in polyphonic audio recordings

Igor Vatolkin

2020/7/19

Implementierung von hybriden Methoden zur Instrumentenerkennung in verrauschten Musikdaten

Igor Vatolkin

M Sc Jurij Kuzmic

2020/5/22

Comparing fuzzy rule based approaches for music genre classification

Frederik Heerde

Igor Vatolkin

Günter Rudolph

2020

See List of Professors in Igor Vatolkin University(Technische Universität Dortmund)

Co-Authors

H-index: 63
Dietmar Jannach

Dietmar Jannach

Alpen-Adria-Universität Klagenfurt

H-index: 53
Christian Igel

Christian Igel

Københavns Universitet

H-index: 45
Bernd Bischl

Bernd Bischl

Ludwig-Maximilians-Universität München

H-index: 44
Mike Preuss

Mike Preuss

Universiteit Leiden

H-index: 39
Rainer Martin

Rainer Martin

Ruhr-Universität Bochum

H-index: 29
Claus Weihs

Claus Weihs

Technische Universität Dortmund

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