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ORCIDMeinard Müller
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- affiliation: Audio Labs Erlangen, Germany
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2020 – today
- 2023
[j53]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)- [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) - [c171]Christof Weiß, Meinard Müller:
Studying Tonal Evolution of Western Choral Music: A Corpus-Based Strategy. CHR 2023: 687-702 - [c170]Michael Krause, Christof Weiß, Meinard Müller:
Soft Dynamic Time Warping for Multi-Pitch Estimation and Beyond. ICASSP 2023: 1-5 - [c169]Nazif Can Tamer, Yigitcan Özer, Meinard Müller, Xavier Serra:
TAPE: An End-to-End Timbre-Aware Pitch Estimator. ICASSP 2023: 1-5 - [c168]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 - [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) - [c167]Yigitcan Özer, Jonathan Hansen, Tim Zunner, Meinard Müller:
Investigating Nonnegative Autoencoders for Efficient Audio Decomposition. EUSIPCO 2022: 254-258 - [c166]Michael Krause, Meinard Müller:
Hierarchical Classification of Singing Activity, Gender, and Type in Complex Music Recordings. ICASSP 2022: 406-410 - [c165]Yigitcan Özer, Meinard Müller:
Source Separation of Piano Concertos with Test-Time Adaptation. ISMIR 2022: 493-500 - [c164]Yigitcan Özer, Matej Istvanek, Vlora Arifi-Müller, Meinard Müller:
Using Activation Functions for Improving Measure-Level Audio Synchronization. ISMIR 2022: 749-756 - [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 - [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 - [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 - 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]Frank Zalkow, Christof Weiß, Meinard Müller:
Exploring Tonal-Dramatic Relationships in Richard Wagner's Ring Cycle. ISMIR 2017: 642-648 - [c122]Jakob Abeßer, Stefan Balke, Klaus Frieler, Martin Pfleiderer, Meinard Müller:
Deep Learning for Jazz Walking Bass Transcription. Semantic Audio 2017 - [c121]Patricio López-Serrano, Christian Dittmar, Meinard Müller:
Mid-Level Audio Features Based on Cascaded Harmonic-Residual-Percussive Separation. Semantic Audio 2017 - [c120]Meinard Müller, Sebastian Rosenzweig, Jonathan Driedger, Frank Scherbaum:
Interactive Fundamental Frequency Estimation with Applications to Ethnomusicological Research. Semantic Audio 2017 - [c119]Frank Zalkow, Christof Weiß, Thomas Prätzlich, Vlora Arifi-Müller, Meinard Müller:
A Multi-Version Approach for Transferring Measure Annotations between Music Recordings. Semantic Audio 2017 - [e7]Christian Dittmar, Jakob Abeßer, Meinard Müller:
AES International Conference Semantic Audio 2017, Erlangen, Germany, June 22-24, 2017. Audio Engineering Society 2017, ISBN 978-1-942220-15-2 [contents] - 2016
- [j26]Christian Dittmar, Meinard Müller:
Reverse Engineering the Amen Break - Score-Informed Separation and Restoration Applied to Drum Recordings. IEEE ACM Trans. Audio Speech Lang. Process. 24(9): 1535-1547 (2016) - [c118]Christian Dittmar, Jonathan Driedger, Meinard Müller, Jouni Paulus:
An experimental approach to generalized Wiener filtering in music source separation. EUSIPCO 2016: 1743-1747 - [c117]Thomas Prätzlich, Meinard Müller:
Triple-based analysis of music alignments without the need of ground-truth annotations. ICASSP 2016: 266-270 - [c116]Stefan Balke, Vlora Arifi-Müller, Lukas Lamprecht, Meinard Müller:
Retrieving audio recordings using musical themes. ICASSP 2016: 281-285 - [c115]Richard Fug, Andreas Niedermeier, Jonathan Driedger, Sascha Disch, Meinard Müller:
Harmonic-percussive-residual sound separation using the structure tensor on spectrograms. ICASSP 2016: 445-449 - [c114]Thomas Prätzlich, Jonathan Driedger, Meinard Müller:
Memory-restricted multiscale dynamic time warping. ICASSP 2016: 569-573 - [c113]Sebastian Ewert, Siying Wang, Meinard Müller, Mark B. Sandler:
Score-Informed Identification of Missing and Extra Notes in Piano Recordings. ISMIR 2016: 30-36 - [c112]Jonathan Driedger, Stefan Balke, Sebastian Ewert, Meinard Müller:
Template-Based Vibrato Analysis in Complex Music Signals. ISMIR 2016: 239-245 - [c111]Stefan Balke, Jonathan Driedger, Jakob Abeßer, Christian Dittmar, Meinard Müller:
Towards Evaluating Multiple Predominant Melody Annotations in Jazz Recordings. ISMIR 2016: 246-252 - [c110]Sebastian Stober, Thomas Prätzlich, Meinard Müller:
Brain Beats: Tempo Extraction from EEG Data. ISMIR 2016: 276-282 - [c109]T. J. Tsai, Thomas Prätzlich, Meinard Müller:
Known Artist Live Song ID: A Hashprint Approach. ISMIR 2016: 427-433 - [c108]Patricio López-Serrano, Christian Dittmar, Jonathan Driedger, Meinard Müller:
Towards Modeling and Decomposing Loop-Based Electronic Music. ISMIR 2016: 502-508 - [c107]Christof Weiß, Vlora Arifi-Müller, Thomas Prätzlich, Rainer Kleinertz, Meinard Müller:
Analyzing Measure Annotations for Western Classical Music Recordings. ISMIR 2016: 517-523 - [i6]Meinard Müller, Elaine Chew, Juan Pablo Bello:
Computational Music Structure Analysis (Dagstuhl Seminar 16092). Dagstuhl Reports 6(2): 147-190 (2016) - 2015
- [b3]Meinard Müller:
Fundamentals of Music Processing - Audio, Analysis, Algorithms, Applications. Springer 2015, ISBN 978-3-319-21944-8, pp. 1-480 - [j25]Daniel Röwenstrunk, Thomas Prätzlich, Thomas Betzwieser, Meinard Müller, Gerd Szwillus, Joachim Veit:
Das Gesamtkunstwerk Oper aus Datensicht - Aspekte des Umgangs mit einer heterogenen Datenlage im BMBF-Projekt "Freischütz Digital". Datenbank-Spektrum 15(1): 65-72 (2015) - [c106]Benjamin Bohl, Thomas Prätzlich, Meinard Müller, Joachim Veit:
Der Gang durch die Domänen zur Erfassung, Aufbereitung und Präsentation von Audiodaten im BMBF-Projekt "Freischütz Digital". DHd 2015 - [c105]Thimmaiah Kuppanda, Norberto Degara, David Worrall, Balaji Thoshkahna, Meinard Müller:
Virtual reality platform for sonification evaluation. ICAD 2015: 117-124 - [c104]