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ORCIDBob L. T. Sturm
Bob L. Sturm
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- affiliation: KTH Royal Institute of Technology, Stockholm
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Journal Articles
- 2024
[j26]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)- 2023
- [j25]Rujing Stacy Huang, Andre Holzapfel, Bob L. T. Sturm, Anna-Kaisa Kaila:
Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music. Trans. Int. Soc. Music. Inf. Retr. 6(1): 43-59 (2023) - 2022
- [j24]Ken Déguernel, Bob L. T. Sturm, Artemi-Maria Gioti, Georgina Born, Oded Ben-Tal, Joëlle Léandre, Aaron Einbond, Iván Paz, Nao Tokui:
Sound Anthology: Content. Comput. Music. J. 46(4): 1 (2022) - [j23]Ken Déguernel, Bob L. T. Sturm, Artemi-Maria Gioti, Georgina Born:
Editors' Notes. Comput. Music. J. 46(4): 5-6 (2022) - [j22]Ken Déguernel, Bob L. T. Sturm, Artemi-Maria Gioti, Georgina Born:
Sound and Video Anthology: Program Notes. Comput. Music. J. 46(4): 112-116 (2022) - [j21]Bob L. T. Sturm, Alexandra L. Uitdenbogerd, Hendrik Vincent Koops, Anna Huang:
Editorial for TISMIR Special Collection: AI and Musical Creativity. Trans. Int. Soc. Music. Inf. Retr. 5(1): 67-70 (2022) - 2020
- [j20]Bhusan Chettri, Emmanouil Benetos, Bob L. T. Sturm:
Dataset Artefacts in Anti-Spoofing Systems: A Case Study on the ASVspoof 2017 Benchmark. IEEE ACM Trans. Audio Speech Lang. Process. 28: 3018-3028 (2020) - 2019
- [j19]Hendrik Purwins, Bob L. Sturm, Bo Li, Juhan Nam, Abeer Alwan:
Introduction to the Issue on Data Science: Machine Learning for Audio Signal Processing. IEEE J. Sel. Top. Signal Process. 13(2): 203-205 (2019) - [j18]Francisco Rodríguez-Algarra, Bob L. Sturm, Simon Dixon:
Characterising Confounding Effects in Music Classification Experiments through Interventions. Trans. Int. Soc. Music. Inf. Retr. 2(1): 52-66 (2019) - 2018
- [j17]Andre Holzapfel, Bob L. Sturm, Mark Coeckelbergh:
Ethical Dimensions of Music Information Retrieval Technology. Trans. Int. Soc. Music. Inf. Retr. 1(1): 44-55 (2018) - 2016
- [j16]Bob L. Sturm:
The "Horse" Inside: Seeking Causes Behind the Behaviors of Music Content Analysis Systems. Comput. Entertain. 14(2): 3:1-3:32 (2016) - 2015
- [j15]Corey Kereliuk, Bob L. Sturm, Jan Larsen:
Deep Learning and Music Adversaries. IEEE Trans. Multim. 17(11): 2059-2071 (2015) - 2014
- [j14]Bob L. Sturm:
A Simple Method to Determine if a Music Information Retrieval System is a "Horse". IEEE Trans. Multim. 16(6): 1636-1644 (2014) - 2013
- [j13]Bob L. Sturm:
Alexander Lerch: An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics. Comput. Music. J. 37(4): 90-91 (2013) - [j12]Bob L. Sturm:
Classification accuracy is not enough - On the evaluation of music genre recognition systems. J. Intell. Inf. Syst. 41(3): 371-406 (2013) - [j11]Bob L. Sturm, Fabien Gouyon:
Revisiting Inter-Genre Similarity. IEEE Signal Process. Lett. 20(11): 1050-1053 (2013) - [j10]Bob L. Sturm, Boris Mailhé, Mark D. Plumbley:
On Theorem 10 in "On Polar Polytopes and the Recovery of Sparse Representations" [Sep 07 3188-3195]. IEEE Trans. Inf. Theory 59(8): 5206-5209 (2013) - 2011
- [j9]Bob L. Sturm, Laurent Daudet:
Recursive nearest neighbor search in a sparse and multiscale domain for comparing audio signals. Signal Process. 91(12): 2836-2851 (2011) - 2010
- [j8]Marcela Morvidone, Bob L. Sturm, Laurent Daudet:
Incorporating scale information with cepstral features: Experiments on musical instrument recognition. Pattern Recognit. Lett. 31(12): 1489-1497 (2010) - [j7]Bob L. Sturm, John J. Shynk:
Sparse Approximation and the Pursuit of Meaningful Signal Models With Interference Adaptation. IEEE Trans. Speech Audio Process. 18(3): 461-472 (2010) - 2009
- [j6]Dan Overholt, John Thompson, Lance Putnam, Bo Bell, Jim Kleban, Bob L. Sturm, JoAnn Kuchera-Morin:
A Multimodal System for Gesture Recognition in Interactive Music Performance. Comput. Music. J. 33(4): 69-82 (2009) - 2008
- [j5]Bob L. Sturm, John J. Shynk, Laurent Daudet, Curtis Roads:
Dark Energy in Sparse Atomic Estimations. IEEE Trans. Speech Audio Process. 16(3): 671-676 (2008) - 2006
- [j4]Bob L. Sturm:
Adaptive Concatenative Sound Synthesis and Its Application to Micromontage Composition. Comput. Music. J. 30(4): 46-66 (2006) - 2005
- [j3]Bob L. Sturm:
Lutz Trautmann and Rudolf Rabenstein: Digital Sound Synthesis by Physical Modeling Using the Functional Transformation Method. Comput. Music. J. 29(2): 91-93 (2005) - 2003
- [j2]Bob L. Sturm:
International Conference on Auditory Display 2002. Comput. Music. J. 27(1): 84-86 (2003) - [j1]Bob L. Sturm:
International Computer Music Conference 2002. Comput. Music. J. 27(2): 103-105 (2003)
Conference and Workshop Papers
- 2024
- [c53]David Dalmazzo, Ken Déguernel, Bob L. T. Sturm:
The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies. EvoMUSART 2024: 52-66 - [c52]Luca Casini, Nicolas Jonason, Bob L. T. Sturm:
Investigating the Viability of Masked Language Modeling for Symbolic Music Generation in abc-notation. EvoMUSART 2024: 84-96 - [c51]Steve Benford, Marco Amerotti, Bob L. Sturm, Juan Martinez Avila:
Negotiating Autonomy and Trust when Performing with an AI Musician. TAS 2024: 6:1-6:10 - 2023
- [c50]Ken Déguernel, Bob L. T. Sturm:
Bias in Favour or Against Computational Creativity: A Survey and Reflection on the Importance of Socio-cultural Context in its Evaluation. ICCC 2023: 162-171 - [c49]Bob L. T. Sturm, Arthur Flexer:
A Review of Validity and Its Relationship to Music Information Research. ISMIR 2023: 47-55 - 2022
- [c48]Ken Déguernel, Bob L. T. Sturm, Hugo Maruri-Aguilar:
Investigating the Relationship Between Liking and Belief in AI Authorship in the Context of Irish Traditional Music. CREAI@AI*IA 2022: 55-68 - [c47]Luca Casini, Bob L. T. Sturm:
Tradformer: A Transformer Model of Traditional Music Transcriptions. IJCAI 2022: 4915-4920 - 2021
- [c46]Rujing Stacy Huang, Bob L. T. Sturm, Andre Holzapfel:
De-centering the West: East Asian Philosophies and the Ethics of Applying Artificial Intelligence to Music. ISMIR 2021: 301-309 - 2020
- [c45]Saumitra Mishra, Emmanouil Benetos, Bob L. Sturm, Simon Dixon:
Reliable Local Explanations for Machine Listening. IJCNN 2020: 1-8 - 2019
- [c44]Bhusan Chettri, Daniel Stoller, Veronica Morfi, Marco A. Martínez Ramírez, Emmanouil Benetos, Bob L. Sturm:
Ensemble Models for Spoofing Detection in Automatic Speaker Verification. INTERSPEECH 2019: 1018-1022 - 2018
- [c43]Saumitra Mishra, Bob L. Sturm, Simon Dixon:
"What are You Listening to?" Explaining Predictions of Deep Machine Listening Systems. EUSIPCO 2018: 2260-2264 - [c42]Bhusan Chettri, Bob L. Sturm:
A Deeper Look at Gaussian Mixture Model Based Anti-Spoofing Systems. ICASSP 2018: 5159-5163 - [c41]Saumitra Mishra, Bob L. Sturm, Simon Dixon:
Understanding a Deep Machine Listening Model Through Feature Inversion. ISMIR 2018: 755-762 - [c40]Bhusan Chettri, Bob L. Sturm, Emmanouil Benetos:
Analysing Replay Spoofing Countermeasure Performance under varied conditions. MLSP 2018: 1-6 - [c39]Bhusan Chettri, Saumitra Mishra, Bob L. Sturm, Emmanouil Benetos:
Analysing The Predictions Of a CNN-Based Replay Spoofing Detection System. SLT 2018: 92-97 - 2017
- [c38]Elio Quinton, Florabelle Spielmann, Bob L. Sturm:
Exploring Trends in Trinidad Steelband Music Through Computational Ethnomusicology. CMMR 2017: 63-75 - [c37]Saumitra Mishra, Bob L. Sturm, Simon Dixon:
Local Interpretable Model-Agnostic Explanations for Music Content Analysis. ISMIR 2017: 537-543 - 2016
- [c36]Simon Colton, Maria Teresa Llano, Rose Hepworth, John William Charnley, Catherine V. Gale, Archie Baron, François Pachet, Pierre Roy, Pablo Gervás, Nick Collins, Bob L. Sturm, Tillman Weyde, Daniel Wolff, James Robert Lloyd:
The "Beyond the Fence" Musical and "Computer Says Show" Documentary. ICCC 2016: 311-321 - [c35]Francisco Rodríguez-Algarra, Bob L. Sturm, Hugo Maruri-Aguilar:
Analysing Scattering-Based Music Content Analysis Systems: Where's the Music?. ISMIR 2016: 344-350 - [c34]Bob L. Sturm:
Revisiting Priorities: Improving MIR Evaluation Practices. ISMIR 2016: 488-494 - 2015
- [c33]Bob L. Sturm, Corey Kereliuk, Jan Larsen:
¿El Caballo Viejo? Latin Genre Recognition with Deep Learning and Spectral Periodicity. MCM 2015: 335-346 - [c32]Corey Kereliuk, Bob L. Sturm, Jan Larsen:
Deep learning, audio adversaries, and music content analysis. WASPAA 2015: 1-5 - 2014
- [c31]Bob L. Sturm, Corey Kereliuk, Aggelos Pikrakis:
A closer look at deep learning neural networks with low-level spectral periodicity features. CIP 2014: 1-6 - [c30]Stefania Serafin, Sofia Dahl, Amalia de Götzen, Cumhur Erkut, Dan Overholt, Hendrik Purwins, Bob L. Sturm:
Sound and Music Computing at Aalborg University in Copenhagen. ICMC 2014 - [c29]Bob L. Sturm, Nick Collins:
The Kiki-Bouba Challenge: Algorithmic Composition for Content-based MIR Research and Development. ISMIR 2014: 21-26 - [c28]Bob L. Sturm, Rolf Bardeli, Thibault Langlois, Valentin Emiya:
Formalizing the Problem of Music Description. ISMIR 2014: 89-94 - 2013
- [c27]Bob L. Sturm:
Making Explicit the Formalism Underlying Evaluation in Music Information Retrieval Research: A Look at the MIREX Automatic Mood Classification Task. CMMR 2013: 89-104 - [c26]Boris Mailhé, Bob L. Sturm, Mark D. Plumbley:
Behavior of greedy sparse representation algorithms on nested supports. ICASSP 2013: 5710-5714 - [c25]Bob L. Sturm:
On music genre classification via compressive sampling. ICME 2013: 1-6 - [c24]Bob L. Sturm:
Music genre recognition with risk and rejection. ICME 2013: 1-6 - [c23]Bob L. Sturm:
Evaluating music emotion recognition: Lessons from music genre recognition? ICME Workshops 2013: 1-6 - 2012
- [c22]Bob L. Sturm:
A Survey of Evaluation in Music Genre Recognition. Adaptive Multimedia Retrieval 2012: 29-66 - [c21]Bob L. Sturm, Mads Græsbøll Christensen:
Comparison of orthogonal matching pursuit implementations. EUSIPCO 2012: 220-224 - [c20]Pardis Noorzad, Bob L. Sturm:
Regression with sparse approximations of data. EUSIPCO 2012: 674-678 - [c19]Bob L. Sturm:
When "exact recovery" is exact recovery in compressed sensing simulation. EUSIPCO 2012: 979-983 - [c18]Bob L. Sturm:
An analysis of the GTZAN music genre dataset. MIRUM 2012: 7-12 - [c17]Bob L. Sturm:
Two systems for automatic music genre recognition: what are they really recognizing? MIRUM 2012: 69-74 - 2011
- [c16]Mads Græsbøll Christensen, Bob L. Sturm:
A perceptually reweighted mixed-norm method for sparse approximation of audio signals. ACSCC 2011: 575-579 - [c15]Bob L. Sturm, Mads Græsbøll Christensen, Rémi Gribonval:
Cyclic pure greedy algorithms for recovering compressively sampled sparse signals. ACSCC 2011: 1143-1147 - [c14]Nick Collins, Bob L. Sturm:
Sound Cross-synthesis and Morphing Using Dictionary-based Methods. ICMC 2011 - 2010
- [c13]Bob L. Sturm, Marcela Morvidone, Laurent Daudet:
Musical instrument identification using multiscale Mel-frequency cepstral coefficients. EUSIPCO 2010: 477-481 - [c12]Bob L. Sturm, Guillaume DeFrance:
Detection and estimation of arrivals in room impulse responses by greedy sparse approximation. EUSIPCO 2010: 1934-1938 - 2009
- [c11]Bob L. Sturm, Laurent Daudet:
On Similarity Search in Audio Signals Using Adaptive Sparse Approximations. Adaptive Multimedia Retrieval 2009: 59-71 - [c10]Bob L. Sturm, John J. Shynk, Dae Hong Kim:
Pruning sparse signal models using interference. CISS 2009: 454-458 - 2008
- [c9]Bob L. Sturm, John J. Shynk:
Interference-driven adaptation in sparse approximation. ACSCC 2008: 241-245 - [c8]Bob L. Sturm, John J. Shynk, Laurent Daudet:
Measuring interference in overcomplete signal representations. CISS 2008: 961-966 - [c7]Bob L. Sturm, John J. Shynk, Steffen Gauglitz:
Agglomerative clustering in sparse atomic decompositions of audio signals. ICASSP 2008: 97-100 - [c6]Bob L. Sturm, Curtis Roads, Aaron McLeran, John J. Shynk:
Analysis, Visualization, and Transformation of audio signals using Dictionary-Based Methods. ICMC 2008 - 2006
- [c5]Bob L. Sturm, Jerry D. Gibson:
Matching Pursuit Decompositions of Non-Noisy Speech Signals Using Several Dictionaries. ICASSP (3) 2006: 456-459 - 2004
- [c4]Bob L. Sturm:
MATConcat: An Application for Exploring Concatenative Sound Synthesis Using MATLAB. ICMC 2004 - [c3]Bob L. Sturm, Jerry D. Gibson:
Signals and Systems Using MATLAB: An Effective Application for Exploring and Teaching Media Signal Processing. ICMC 2004 - [c2]Bob L. Sturm, Stephen Travis Pope:
Spectral Characteristics of the Musical Iced Tea Can. ICMC 2004 - 2002
- [c1]Bob L. Sturm:
Water Music: Sonificiation of Ocean Buoy Spectral Data. ICMC 2002
Data and Artifacts
- 2020
- [d1]Bhusan Chettri, Emmanouil Benetos, Bob L. Sturm:
Speech endpoint annotations and artefact details for ASVspoof 2017 version 2.0 dataset. Zenodo, 2020
Informal and Other Publications
- 2024
- [i20]Nicolas Jonason, Luca Casini, Bob L. T. Sturm:
SYMPLEX: Controllable Symbolic Music Generation using Simplex Diffusion with Vocabulary Priors. CoRR abs/2405.12666 (2024) - [i19]Nicolas Jonason, Luca Casini, Bob L. T. Sturm:
Steer-by-prior Editing of Symbolic Music Loops. CoRR abs/2408.02434 (2024) - 2023
- [i18]Bob L. T. Sturm, Arthur Flexer:
Validity in Music Information Research Experiments. CoRR abs/2301.01578 (2023) - [i17]Nicolas Jonason, Bob L. T. Sturm:
Exploring Softly Masked Language Modelling for Controllable Symbolic Music Generation. CoRR abs/2305.03530 (2023) - [i16]Nicolas Jonason, Xin Wang, Erica Cooper, Lauri Juvela, Bob L. T. Sturm, Junichi Yamagishi:
DDSP-based Neural Waveform Synthesis of Polyphonic Guitar Performance from String-wise MIDI Input. CoRR abs/2309.07658 (2023) - [i15]Nicolas Jonason, Luca Casini, Carl Thomé, Bob L. T. Sturm:
Retrieval Augmented Generation of Symbolic Music with LLMs. CoRR abs/2311.10384 (2023) - 2022
- [i14]Simon Colton, Maria Teresa Llano, Rose Hepworth, John William Charnley, Catherine V. Gale, Archie Baron, François Pachet, Pierre Roy, Pablo Gervás, Nick Collins, Bob L. Sturm, Tillman Weyde, Daniel Wolff, James Robert Lloyd:
The Beyond the Fence Musical and Computer Says Show Documentary. CoRR abs/2206.03224 (2022) - [i13]Nicolas Jonason, Bob L. T. Sturm:
TimbreCLIP: Connecting Timbre to Text and Images. CoRR abs/2211.11225 (2022) - [i12]Nicolas Jonason, Bob L. T. Sturm:
Audio Latent Space Cartography. CoRR abs/2212.02610 (2022) - 2020
- [i11]Saumitra Mishra, Emmanouil Benetos, Bob L. Sturm, Simon Dixon:
Reliable Local Explanations for Machine Listening. CoRR abs/2005.07788 (2020) - [i10]Bhusan Chettri, Emmanouil Benetos, Bob L. T. Sturm:
Dataset artefacts in anti-spoofing systems: a case study on the ASVspoof 2017 benchmark. CoRR abs/2010.07913 (2020) - 2019
- [i9]Bhusan Chettri, Daniel Stoller, Veronica Morfi, Marco A. Martínez Ramírez, Emmanouil Benetos, Bob L. Sturm:
Ensemble Models for Spoofing Detection in Automatic Speaker Verification. CoRR abs/1904.04589 (2019) - [i8]Saumitra Mishra, Daniel Stoller, Emmanouil Benetos, Bob L. Sturm, Simon Dixon:
GAN-based Generation and Automatic Selection of Explanations for Neural Networks. CoRR abs/1904.09533 (2019) - 2018
- [i7]Bhusan Chettri, Saumitra Mishra, Bob L. Sturm, Emmanouil Benetos:
A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing. CoRR abs/1805.09164 (2018) - 2016
- [i6]Bob L. Sturm, João Felipe Santos, Oded Ben-Tal, Iryna Korshunova:
Music transcription modelling and composition using deep learning. CoRR abs/1604.08723 (2016) - [i5]Bob L. Sturm:
The "Horse" Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems. CoRR abs/1606.03044 (2016) - 2015
- [i4]Corey Kereliuk, Bob L. Sturm, Jan Larsen:
Deep Learning and Music Adversaries. CoRR abs/1507.04761 (2015) - 2014
- [i3]Fabien Gouyon, Bob L. Sturm, João Lobato Oliveira, Nuno Hespanhol, Thibault Langlois:
On Evaluation Validity in Music Autotagging. CoRR abs/1410.0001 (2014) - 2013
- [i2]Bob L. Sturm:
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use. CoRR abs/1306.1461 (2013) - 2011
- [i1]Bob L. Sturm:
A Study on Sparse Vector Distributions and Recovery from Compressed Sensing. CoRR abs/1103.6246 (2011)
Coauthor Index
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