Archiv der Kategorie: MIR Research

Paper on multi-modal music classification using six modalities published in TISMIR

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), pp.1–19, 2022. Abstract: Every type of musical data (audio, symbolic, lyrics, etc.) has its … Weiterlesen

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Paper on EAR Drummer accepted for TISMIR special collection on AI and Musical Creativity

F. Ostermann, I. Vatolkin, and G. Rudolph: Evaluating Creativity in Automatic Reactive Accompaniment of Jazz Improvisation Abstract: Music generating computer programs can support jazz musicians and students during performance and practice, for instance by providing accompaniment for solo improvisation. However, … Weiterlesen

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Paper on multi-modal music classification accepted for Entropy

The following paper was accepted for Entropy: B. Wilkes, I. Vatolkin, and H. Müller: Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition

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Job offer for a student assistant

For assistance during software project „Music Informatics“, a position as a student assistant (8 hours per week) is offered at the Chair of Algorithm Engineering, Department of Computer Science, TU Dortmund. Please see the full description in German.

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AMUSE paper accepted for SIGIR

The following paper was accepted for SIGIR conference: I. Vatolkin, P. Ginsel, and G. Rudolph: Advancements in the Music Information Retrieval Framework AMUSE over the Last Decade Before the presentation at SIGIR, we will update the user manual (the current … Weiterlesen

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AMUSE Repository Moved

The repository of Advanced MUSic Explorer has moved to: https://github.com/AdvancedMUSicExplorer/AMUSE

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Master Thesis on Instrument Recognition

The thesis „Benedikt Adrian: Implementierung von hybriden Methoden zur Instrumentenerkennung in verrauschten Musikdaten“ (Implementation of Hybrid Methods for Instrument Recognition in Noisy Music Data, PDF in German) applies CNNs together with shallow classifiers for the recognition of instruments in polyphonic … Weiterlesen

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Two Papers Presented at WCCI 2020

Two papers were presented during WCCI 2020: Vatolkin, I.: Evolutionary Approximation of Instrumental Texture in Polyphonic Audio Recordings [evaluates new features based on evolutionary approximation of instrumental texture for instrument and genre recognition] Ginsel, P., Vatolkin, I. Rudolph, G.: Analysis … Weiterlesen

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New book: Applications in Statistical Computing

The book Bauer, N., Ickstadt, K., Lübke, K., Szepannek, G., Trautmann, H., Vichi, M (Eds.): Applications in Statistical Computing: From Music Data Analysis to Industrial Quality Improvement, Springer, has recently been published. This volume presents a selection of research papers … Weiterlesen

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Bachelor thesis on music genre classification with Fuzzy KNN

The thesis „Philipp Ginsel: Strukturelle Komplexitätsmerkmale zur Musikgenre-Klassifikation mit Fuzzy-k-nearest-Neighbors“ (Structural complexity features for music genre classification with Fuzzy-k-nearest-Neighbors, PDF in German) examines different settings for extraction of structural complexity for prediction of music genres with Fuzzy KNN. The tested … Weiterlesen

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