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ORCIDMeinard Müller
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- affiliation: Audio Labs Erlangen, Germany
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2020 – today
- 2024
[j57]Yigitcan Özer
, Meinard Müller:
Source Separation of Piano Concertos Using Musically Motivated Augmentation Techniques. IEEE ACM Trans. Audio Speech Lang. Process. 32: 1214-1225 (2024)- [j56]Simon J. Schwär, Michael Krause, Michael Fast, Sebastian Rosenzweig, Frank Scherbaum, Meinard Müller:
A Dataset of Larynx Microphone Recordings for Singing Voice Reconstruction. Trans. Int. Soc. Music. Inf. Retr. 7(1): 30-43 (2024) - [j55]Meinard Müller, Simon Dixon, Anja Volk, Bob L. T. Sturm, Preeti Rao, Mark Gotham:
Introducing the TISMIR Education Track: What, Why, How? Trans. Int. Soc. Music. Inf. Retr. 7(1): 85-98 (2024) - [c182]Johannes Zeitler, Michael Krause, Meinard Müller:
Soft Dynamic Time Warping with Variable Step Weights. ICASSP 2024: 356-360 - [c181]Ben Maman, Johannes Zeitler, Meinard Müller, Amit H. Bermano:
Performance Conditioning for Diffusion-Based Multi-Instrument Music Synthesis. ICASSP 2024: 5045-5049 - 2023
- [j54]Sebastian Rosenzweig, Frank Scherbaum, Meinard Müller:
Computer-assisted Analysis of Field Recordings: A Case Study of Georgian Funeral Songs. ACM Journal on Computing and Cultural Heritage 16(1): 1-16 (2023) - [j53]Simon J. Schwär, Meinard Müller:
Multi-Scale Spectral Loss Revisited. IEEE Signal Process. Lett. 30: 1712-1716 (2023) - [j52]Michael Krause, Meinard Müller:
Hierarchical Classification for Instrument Activity Detection in Orchestral Music Recordings. IEEE ACM Trans. Audio Speech Lang. Process. 31: 2567-2578 (2023) - [j51]Jakob Abeßer, Sascha Grollmisch, Meinard Müller:
How Robust are Audio Embeddings for Polyphonic Sound Event Tagging? IEEE ACM Trans. Audio Speech Lang. Process. 31: 2658-2667 (2023) - [j50]Ching-Yu Chiu, Meinard Müller, Matthew E. P. Davies, Alvin Wen-Yu Su, Yi-Hsuan Yang:
Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music. IEEE ACM Trans. Audio Speech Lang. Process. 31: 2824-2835 (2023) - [j49]Yigitcan Özer, Simon J. Schwär, Vlora Arifi-Müller, Jeremy Lawrence, Emre Sen, Meinard Müller:
Piano Concerto Dataset (PCD): A Multitrack Dataset of Piano Concertos. Trans. Int. Soc. Music. Inf. Retr. 6(1): 75-88 (2023) - [j48]Christof Weiß, Vlora Arifi-Müller, Michael Krause, Frank Zalkow, Stephanie Klauk, Rainer Kleinertz, Meinard Müller:
Wagner Ring Dataset: A Complex Opera Scenario for Music Processing and Computational Musicology. Trans. Int. Soc. Music. Inf. Retr. 6(1): 135-149 (2023) - [j47]Yi-Jen Shih, Shih-Lun Wu, Frank Zalkow, Meinard Müller, Yi-Hsuan Yang:
Theme Transformer: Symbolic Music Generation With Theme-Conditioned Transformer. IEEE Trans. Multim. 25: 3495-3508 (2023) - [c180]Christof Weiß, Meinard Müller:
Studying Tonal Evolution of Western Choral Music: A Corpus-Based Strategy. CHR 2023: 687-702 - [c179]Meinard Müller, Frank Zalkow:
FMP Notebooks. GI-Jahrestagung 2023: 785-794 - [c178]Christof Weiß, Meinard Müller, Stephanie Klauk, Rainer Kleinertz:
Neue Wege für die Musikforschung. GI-Jahrestagung 2023: 805-814 - [c177]Peter Meier, Simon J. Schwär, Gerhard Krump, Meinard Müller:
Evaluating Real-Time Pitch Estimation Algorithms for Creative Music Game Interaction. GI-Jahrestagung 2023: 873-882 - [c176]Stephanie Klauk, Rainer Kleinertz, Pascal Schmolenzky, Christof Weiß, Meinard Müller:
Perspektiven computergestützter harmonischer Analyse: Beethovens op. 14 Nr. 1 als Gegenstand gattungsübergreifender Korpusanalyse. GI-Jahrestagung 2023: 883-889 - [c175]Michael Krause, Christof Weiß, Meinard Müller:
Soft Dynamic Time Warping for Multi-Pitch Estimation and Beyond. ICASSP 2023: 1-5 - [c174]Nazif Can Tamer, Yigitcan Özer, Meinard Müller, Xavier Serra:
TAPE: An End-to-End Timbre-Aware Pitch Estimator. ICASSP 2023: 1-5 - [c173]Frank Zalkow, Prachi Govalkar, Meinard Müller, Emanuël A. P. Habets, Christian Dittmar:
Evaluating Speech-Phoneme Alignment and its Impact on Neural Text-To-Speech Synthesis. ICASSP 2023: 1-5 - [c172]Nazif Can Tamer, Yigitcan Özer, Meinard Müller, Xavier Serra:
High-Resolution Violin Transcription Using Weak Labels. ISMIR 2023: 223-230 - [c171]Michael Krause, Sebastian Strahl, Meinard Müller:
Weakly Supervised Multi-Pitch Estimation Using Cross-Version Alignment. ISMIR 2023: 289-296 - [c170]Johannes Zeitler, Simon Deniffel, Michael Krause, Meinard Müller:
Stabilizing Training With Soft Dynamic Time Warping: A Case Study for Pitch Class Estimation With Weakly Aligned Targets. ISMIR 2023: 433-439 - [c169]Michael Krause, Christof Weiß, Meinard Müller:
A Cross-Version Approach to Audio Representation Learning for Orchestral Music. ISMIR 2023: 832-839 - [i19]Michael Krause, Christof Weiß, Meinard Müller:
Soft Dynamic Time Warping for Multi-Pitch Estimation and Beyond. CoRR abs/2304.05032 (2023) - [i18]Johannes Zeitler, Simon Deniffel, Michael Krause, Meinard Müller:
Stabilizing Training with Soft Dynamic Time Warping: A Case Study for Pitch Class Estimation with Weakly Aligned Targets. CoRR abs/2308.05429 (2023) - [i17]Ching-Yu Chiu, Meinard Müller, Matthew E. P. Davies, Alvin Wen-Yu Su, Yi-Hsuan Yang:
Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music. CoRR abs/2308.10355 (2023) - [i16]Ben Maman, Johannes Zeitler, Meinard Müller, Amit H. Bermano:
Performance Conditioning for Diffusion-Based Multi-Instrument Music Synthesis. CoRR abs/2309.12283 (2023) - 2022
- [j46]Ching-Yu Chiu, Meinard Müller, Matthew E. P. Davies, Alvin Wen-Yu Su, Yi-Hsuan Yang:
An Analysis Method for Metric-Level Switching in Beat Tracking. IEEE Signal Process. Lett. 29: 2153-2157 (2022) - [j45]Stefan Balke, Julian Reck, Christof Weiß, Jakob Abeßer, Meinard Müller:
JSD: A Dataset for Structure Analysis in Jazz Music. Trans. Int. Soc. Music. Inf. Retr. 5(1): 156-172 (2022) - [j44]Alessandro Ilic Mezza, Emanuël A. P. Habets, Meinard Müller, Augusto Sarti:
Unsupervised Domain Adaptation via Principal Subspace Projection for Acoustic Scene Classification. J. Signal Process. Syst. 94(2): 197-213 (2022) - [c168]Yigitcan Özer, Jonathan Hansen, Tim Zunner, Meinard Müller:
Investigating Nonnegative Autoencoders for Efficient Audio Decomposition. EUSIPCO 2022: 254-258 - [c167]Michael Krause, Meinard Müller:
Hierarchical Classification of Singing Activity, Gender, and Type in Complex Music Recordings. ICASSP 2022: 406-410 - [c166]Yigitcan Özer, Meinard Müller:
Source Separation of Piano Concertos with Test-Time Adaptation. ISMIR 2022: 493-500 - [c165]Yigitcan Özer, Matej Istvanek, Vlora Arifi-Müller, Meinard Müller:
Using Activation Functions for Improving Measure-Level Audio Synchronization. ISMIR 2022: 749-756 - [c164]Peter Meier, Simon J. Schwär, Sebastian Rosenzweig, Meinard Müller:
Real-Time MIR Algorithms for Music-Reactive Game World Generation. MuC (Workshopband) 2022 - [d11]Meinard Müller, Sebastian Rosenzweig:
PCP Notebooks: A Preparation Course for Python with a Focus on Signal Processing. Zenodo, 2022
- [i15]Ching-Yu Chiu, Meinard Müller, Matthew E. P. Davies, Alvin Wen-Yu Su, Yi-Hsuan Yang:
An Analysis Method for Metric-Level Switching in Beat Tracking. CoRR abs/2210.06817 (2022) - [i14]Meinard Müller, Rachel M. Bittner, Juhan Nam:
Deep Learning and Knowledge Integration for Music Audio Analysis (Dagstuhl Seminar 22082). Dagstuhl Reports 12(2): 103-133 (2022) - 2021
- [b4]Meinard Müller:
Fundamentals of Music Processing - Using Python and Jupyter Notebooks, Second Edition. Springer 2021, ISBN 978-3-030-69807-2, pp. 1-495 - [j43]Christof Weiß, Frank Zalkow, Vlora Arifi-Müller, Meinard Müller, Hendrik Vincent Koops, Anja Volk, Harald G. Grohganz:
Schubert Winterreise Dataset: A Multimodal Scenario for Music Analysis. ACM Journal on Computing and Cultural Heritage 14(2): 25:1-25:18 (2021) - [j42]Meinard Müller, Frank Zalkow:
libfmp: A Python Package for Fundamentals of Music Processing. J. Open Source Softw. 6(63): 3326 (2021) - [j41]Meinard Müller, Yigitcan Özer, Michael Krause, Thomas Prätzlich, Jonathan Driedger:
Sync Toolbox: A Python Package for Efficient, Robust, and Accurate Music Synchronization. J. Open Source Softw. 6(64): 3434 (2021) - [j40]Meinard Müller, Brian McFee, Katherine M. Kinnaird:
Interactive Learning of Signal Processing Through Music: Making Fourier Analysis Concrete for Students. IEEE Signal Process. Mag. 38(3): 73-84 (2021) - [j39]Frank Zalkow, Meinard Müller:
CTC-Based Learning of Chroma Features for Score-Audio Music Retrieval. IEEE ACM Trans. Audio Speech Lang. Process. 29: 2957-2971 (2021) - [j38]Michael Krause, Meinard Müller, Christof Weiß:
Towards Leitmotif Activity Detection in Opera Recordings. Trans. Int. Soc. Music. Inf. Retr. 4(1): 127-140 (2021) - [c163]Sebastian Rosenzweig, Simon J. Schwär, Jonathan Driedger, Meinard Müller:
Adaptive Pitch-Shifting with Applications to Intonation Adjustment in a Cappella Recordings. DAFx 2021: 121-128 - [c162]Igor Vatolkin, Marcel Koch, Meinard Müller:
A Multi-objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation. EvoMUSART 2021: 327-343 - [c161]Igor Vatolkin, Fabian Ostermann, Meinard Müller:
An evolutionary multi-objective feature selection approach for detecting music segment boundaries of specific types. GECCO 2021: 1061-1069 - [c160]Sebastian Rosenzweig, Frank Scherbaum, Meinard Müller:
Reliability Assessment of Singing Voice F0-Estimates Using Multiple Algorithms. ICASSP 2021: 261-265 - [c159]Mark Gotham, Rainer Kleinertz, Christof Weiss, Meinard Müller, Stephanie Klauk:
What if the 'When' Implies the 'What'?: Human harmonic analysis datasets clarify the relative role of the separate steps in automatic tonal analysis. ISMIR 2021: 229-236 - [c158]Simon J. Schwär, Sebastian Rosenzweig, Meinard Müller:
A Differentiable Cost Measure for Intonation Processing in Polyphonic Music. ISMIR 2021: 626-633 - [c157]Christof Weiss, Johannes Zeitler, Tim Zunner, Florian Schuberth, Meinard Müller:
Learning Pitch-Class Representations from Score-Audio Pairs of Classical Music. ISMIR 2021: 746-753 - [d10]Meinard Müller, Yigitcan Özer, Michael Krause, Thomas Prätzlich, Jonathan Driedger:
Sync Toolbox: A Python Package for Efficient, Robust, and Accurate Music Synchronization. Zenodo, 2021 - [d9]Meinard Müller, Sebastian Rosenzweig:
meinardmueller/PCP: v1.1.0. Zenodo, 2021 - [d8]Meinard Müller, Sebastian Rosenzweig:
meinardmueller/PCP: v1.1.1. Zenodo, 2021 - [d7]Meinard Müller, Frank Zalkow:
libfmp: A Python Package for Fundamentals of Music Processing. Zenodo, 2021 - [d6]Christof Weiß, Frank Zalkow, Vlora Arifi-Müller, Meinard Müller, Hendrik Vincent Koops, Anja Volk, Harald G. Grohganz:
Schubert Winterreise Dataset. Zenodo, 2021 - [i13]Jakob Abeßer, Meinard Müller:
Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning. CoRR abs/2110.13586 (2021) - [i12]Yi-Jen Shih, Shih-Lun Wu, Frank Zalkow, Meinard Müller, Yi-Hsuan Yang:
Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer. CoRR abs/2111.04093 (2021) - 2020
- [j37]Christof Weiß, Hendrik Schreiber, Meinard Müller:
Local Key Estimation in Music Recordings: A Case Study Across Songs, Versions, and Annotators. IEEE ACM Trans. Audio Speech Lang. Process. 28: 2919-2932 (2020) - [j36]Sebastian Rosenzweig, Frank Scherbaum, David Shugliashvili, Vlora Arifi-Müller, Meinard Müller:
Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology. Trans. Int. Soc. Music. Inf. Retr. 3(1): 31-41 (2020) - [j35]Sebastian Rosenzweig, Helena Cuesta, Christof Weiß, Frank Scherbaum, Emilia Gómez, Meinard Müller:
Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing. Trans. Int. Soc. Music. Inf. Retr. 3(1): 98-110 (2020) - [j34]Hendrik Schreiber, Julián Urbano, Meinard Müller:
Music Tempo Estimation: Are We Done Yet? Trans. Int. Soc. Music. Inf. Retr. 3(1): 111 (2020) - [j33]Frank Zalkow, Stefan Balke, Vlora Arifi-Müller, Meinard Müller:
MTD: A Multimodal Dataset of Musical Themes for MIR Research. Trans. Int. Soc. Music. Inf. Retr. 3(1): 180-192 (2020) - [c156]Alessandro Ilic Mezza, Emanuël Anco Peter Habets, Meinard Müller, Augusto Sarti:
Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching. EUSIPCO 2020: 11-15 - [c155]Hendrik Schreiber, Christof Weiß, Meinard Müller:
Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert's Winterreise. ICASSP 2020: 501-505 - [c154]Frank Zalkow, Meinard Müller:
Using Weakly Aligned Score-Audio Pairs to Train Deep Chroma Models for Cross-Modal Music Retrieval. ISMIR 2020: 184-191 - [c153]Christof Weiss, Stephanie Klauk, Mark Gotham, Meinard Müller, Rainer Kleinertz:
Discourse not Dualism: An Interdisciplinary Dialogue on Sonata Form in Beethoven's Early Piano Sonatas. ISMIR 2020: 199-206 - [c152]Michael Krause, Frank Zalkow, Julia Zalkow, Christof Weiss, Meinard Müller:
Classifying Leitmotifs in Recordings of Operas by Richard Wagner. ISMIR 2020: 473-480 - [c151]Hendrik Schreiber, Frank Zalkow, Meinard Müller:
Modeling and Estimating Local Tempo: A Case Study on Chopin's Mazurkas. ISMIR 2020: 773-779 - [c150]Alessandro Ilic Mezza, Emanuël A. P. Habets, Meinard Müller, Augusto Sarti:
Feature Projection-Based Unsupervised Domain Adaptation for Acoustic Scene Classification. MLSP 2020: 1-6 - [d5]Christof Weiß, Frank Zalkow, Vlora Arifi-Müller, Meinard Müller, Hendrik Vincent Koops, Anja Volk, Harald G. Grohganz:
Schubert Winterreise Dataset. Zenodo, 2020 - [d4]Christof Weiß, Frank Zalkow, Vlora Arifi-Müller, Meinard Müller, Hendrik Vincent Koops, Anja Volk, Harald G. Grohganz:
Schubert Winterreise Dataset. Zenodo, 2020 - [d3]Christof Weiß, Frank Zalkow, Vlora Arifi-Müller, Meinard Müller, Hendrik Vincent Koops, Anja Volk, Harald G. Grohganz:
Schubert Winterreise Dataset. Zenodo, 2020 - [i11]Thitaree Tanprasert, Teerapat Jenrungrot, Meinard Müller, T. J. Tsai:
MIDI-Sheet Music Alignment Using Bootleg Score Synthesis. CoRR abs/2004.10345 (2020) - [i10]Alessandro Ilic Mezza, Emanuël Anco Peter Habets, Meinard Müller, Augusto Sarti:
Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching. CoRR abs/2005.00145 (2020)
2010 – 2019
- 2019
- [j32]Meinard Müller, Bryan Pardo, Gautham J. Mysore, Vesa Välimäki:
Recent Advances in Music Signal Processing [From the Guest Editors]. IEEE Signal Process. Mag. 36(1): 17-19 (2019) - [j31]Meinard Müller, Andreas Arzt, Stefan Balke, Matthias Dorfer, Gerhard Widmer:
Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies. IEEE Signal Process. Mag. 36(1): 52-62 (2019) - [c149]Frank Zalkow, Stefan Balke, Meinard Müller:
Evaluating Salience Representations for Cross-modal Retrieval of Western Classical Music Recordings. ICASSP 2019: 331-335 - [c148]Christof Weiß, Fabian Brand, Meinard Müller:
Mid-level Chord Transition Features for Musical Style Analysis. ICASSP 2019: 341-345 - [c147]Jakob Abeßer, Meinard Müller:
Fundamental Frequency Contour Classification: A Comparison between Hand-crafted and CNN-based Features. ICASSP 2019: 486-490 - [c146]Thitaree Tanprasert, Teerapat Jenrungrot, Meinard Müller, Timothy Tsai:
MIDI-Sheet Music Alignment Using Bootleg Score Synthesis. ISMIR 2019: 91-98 - [c145]Jonathan Driedger, Hendrik Schreiber, W. Bas de Haas, Meinard Müller:
Towards Automatically Correcting Tapped Beat Annotations for Music Recordings. ISMIR 2019: 200-207 - [c144]Christof Weiss, Sebastian J. Schlecht, Sebastian Rosenzweig, Meinard Müller:
Towards Measuring Intonation Quality of Choir Recordings: A Case Study on Bruckner's Locus Iste. ISMIR 2019: 276-283 - [c143]Sebastian Rosenzweig, Frank Scherbaum, Meinard Müller:
Detecting Stable Regions in Frequency Trajectories for Tonal Analysis of Traditional Georgian Vocal Music. ISMIR 2019: 352-359 - [c142]Meinard Müller, Frank Zalkow:
FMP Notebooks: Educational Material for Teaching and Learning Fundamentals of Music Processing. ISMIR 2019: 573-580 - [c141]Michael Taenzer, Jakob Abeßer, Stylianos I. Mimilakis, Christof Weiss, Meinard Müller:
Investigating CNN-based Instrument Family Recognition for Western Classical Music Recordings. ISMIR 2019: 612-619 - [c140]Stylianos I. Mimilakis, Christof Weiss, Vlora Arifi-Müller, Jakob Abeßer, Meinard Müller:
Cross-version Singing Voice Detection in Opera Recordings: Challenges for Supervised Learning. PKDD/ECML Workshops (2) 2019: 429-436 - [i9]Meinard Müller, Andreas Arzt, Stefan Balke, Matthias Dorfer, Gerhard Widmer:
Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies. CoRR abs/1902.04397 (2019) - [i8]Hendrik Schreiber, Meinard Müller:
Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters. CoRR abs/1903.10839 (2019) - [i7]Meinard Müller, Emilia Gómez, Yi-Hsuan Yang:
Computational Methods for Melody and Voice Processing in Music Recordings (Dagstuhl Seminar 19052). Dagstuhl Reports 9(1): 125-177 (2019) - 2018
- [j30]Stefan Balke, Christian Dittmar, Jakob Abeßer, Klaus Frieler, Martin Pfleiderer, Meinard Müller:
Bridging the Gap: Enriching YouTube Videos with Jazz Music Annotations. Frontiers Digit. Humanit. 5: 1 (2018) - [j29]Nooshin Haji Ghassemi, Julius Hannink, Christine F. Martindale, Heiko Gassner, Meinard Müller, Jochen Klucken, Björn M. Eskofier:
Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson's Disease. Sensors 18(1): 145 (2018) - [j28]Chih-Wei Wu, Christian Dittmar, Carl Southall, Richard Vogl, Gerhard Widmer, Jason Hockman, Meinard Müller, Alexander Lerch:
A Review of Automatic Drum Transcription. IEEE ACM Trans. Audio Speech Lang. Process. 26(9): 1457-1483 (2018) - [c139]Christian Dittmar, Patricio López-Serrano, Meinard Müller:
Unifying Local and Global Methods for Harmonic-Percussive Source Separation. ICASSP 2018: 176-180 - [c138]Hendrik Schreiber, Meinard Müller:
A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network. ISMIR 2018: 98-105 - [c137]Jakob Abeßer, Stefan Balke, Meinard Müller:
Improving Bass Saliency Estimation Using Transfer Learning and Label Propagation. ISMIR 2018: 306-312 - [c136]Hendrik Schreiber, Meinard Müller:
A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music. ISMIR 2018: 409-415 - [c135]Christof Weiss, Stefan Balke, Jakob Abeßer, Meinard Müller:
Computational Corpus Analysis: A Case Study on Jazz Solos. ISMIR 2018: 416-423 - [d2]Hendrik Schreiber, Meinard Müller:
Tempo-CNN Training Datasets (LMD Tempo, GiantSteps MTG Tempo, EBall). Zenodo, 2018 - [d1]Hendrik Schreiber, Meinard Müller:
New crowdsourced annotations for the GiantSteps Tempo dataset. Zenodo, 2018 - 2017
- [j27]T. J. Tsai, Thomas Prätzlich, Meinard Müller:
Known-Artist Live Song Identification Using Audio Hashprints. IEEE Trans. Multim. 19(7): 1569-1582 (2017) - [c134]Patricio López-Serrano, Christian Dittmar, Meinard Müller:
Finding Drum Breaks in Digital Music Recordings. CMMR 2017: 111-122 - [c133]Christine F. Martindale, Martin Strauss, Heiko Gassner, Julia List, Meinard Müller, Jochen Klucken, Zacharias Kohl, Björn M. Eskofier:
Segmentation of gait sequences using inertial sensor data in hereditary spastic paraplegia. EMBC 2017: 1266-1269 - [c132]Meinard Müller, Christian Dittmar:
Workshop WS01 "Musik trifft Informatik". GI-Jahrestagung 2017: 47 - [c131]Stefan Balke, Paul Bießmann, Sebastian Trump, Meinard Müller:
Konzeption einer webbasierten Benutzerschnittstelle zur Unterstützung des Jazz-Piano Unterrichts. GI-Jahrestagung 2017: 61-73 - [c130]Stefan Balke, Manuel Hiemer, Peter K. Schwab, Vlora Arifi-Müller, Klaus Meyer-Wegener, Meinard Müller:
Die Oper als Multimediaszenario. GI-Jahrestagung 2017: 75-86 - [c129]Frank Scherbaum, Meinard Müller, Sebastian Rosenzweig:
Rechnergestützte Musikethnologie am Beispiel historischer Aufnahmen mehrstimmiger georgischer Vokalmusik. GI-Jahrestagung 2017: 163-175 - [c128]Christof Weiß, Frank Zalkow, Meinard Müller, Stephanie Klauk, Rainer Kleinertz:
Versionsübergreifende Visualisierung harmonischer Verläufe. GI-Jahrestagung 2017: 205-217 - [c127]Meinard Müller, Stefan Balke, Christof Weiß:
Tutorial TUT01 "Automatisierte Methoden der Musikverarbeitung". GI-Jahrestagung 2017: 2583-2584 - [c126]Stefan Balke, Christian Dittmar, Jakob Abeßer, Meinard Müller:
Data-driven solo voice enhancement for jazz music retrieval. ICASSP 2017: 196-200 - [c125]T. J. Tsai, Steven K. Tjoa, Meinard Müller:
Make Your Own Accompaniment: Adapting Full-Mix Recordings to Match Solo-Only User Recordings. ISMIR 2017: 79-86 - [c124]Hendrik Schreiber, Meinard Müller:
A Post-Processing Procedure for Improving Music Tempo Estimates Using Supervised Learning. ISMIR 2017: 235-242 - [c123]