Water Music: Sonificiation of Ocean Buoy Spectral Data. - Bob L. Sturm (ICMC, 2002)

International Conference on Auditory Display 2002. - Bob L. Sturm (Comput. Music. J., 2003)

International Computer Music Conference 2002. - Bob L. Sturm (Comput. Music. J., 2003)

MATConcat: An Application for Exploring Concatenative Sound Synthesis Using MATLAB. - Bob L. Sturm (ICMC, 2004)

Signals and Systems Using MATLAB: An Effective Application for Exploring and Teaching Media Signal Processing. - Bob L. Sturm; Jerry D. Gibson (ICMC, 2004)

Spectral Characteristics of the Musical Iced Tea Can. - Bob L. Sturm; Stephen Travis Pope (ICMC, 2004)

Lutz Trautmann and Rudolf Rabenstein: Digital Sound Synthesis by Physical Modeling Using the Functional Transformation Method. - Bob L. Sturm (Comput. Music. J., 2005)

Matching Pursuit Decompositions of Non-Noisy Speech Signals Using Several Dictionaries. - Bob L. Sturm; Jerry D. Gibson (ICASSP (3), 2006)

Adaptive Concatenative Sound Synthesis and Its Application to Micromontage Composition. - Bob L. Sturm (Comput. Music. J., 2006)

Interference-driven adaptation in sparse approximation. - Bob L. Sturm; John J. Shynk (ACSCC, 2008)

Measuring interference in overcomplete signal representations. - Bob L. Sturm; John J. Shynk; Laurent Daudet (CISS, 2008)

Agglomerative clustering in sparse atomic decompositions of audio signals. - Bob L. Sturm; John J. Shynk; Steffen Gauglitz (ICASSP, 2008)

Analysis, Visualization, and Transformation of audio signals using Dictionary-Based Methods. - Bob L. Sturm; Curtis Roads; Aaron McLeran; John J. Shynk (ICMC, 2008)

Dark Energy in Sparse Atomic Estimations. - Bob L. Sturm; John J. Shynk; Laurent Daudet; Curtis Roads (IEEE Trans. Speech Audio Process., 2008)

On Similarity Search in Audio Signals Using Adaptive Sparse Approximations. - Bob L. Sturm; Laurent Daudet (Adaptive Multimedia Retrieval, 2009)

Pruning sparse signal models using interference. - Bob L. Sturm; John J. Shynk; Dae Hong Kim (CISS, 2009)

A Multimodal System for Gesture Recognition in Interactive Music Performance. - Dan Overholt; John Thompson; Lance Putnam; Bo Bell; Jim Kleban; Bob L. Sturm; JoAnn Kuchera-Morin (Comput. Music. J., 2009)

Detection and estimation of arrivals in room impulse responses by greedy sparse approximation. - Bob L. Sturm; Guillaume DeFrance (EUSIPCO, 2010)

Musical instrument identification using multiscale Mel-frequency cepstral coefficients. - Bob L. Sturm; Marcela Morvidone; Laurent Daudet (EUSIPCO, 2010)

Incorporating scale information with cepstral features: Experiments on musical instrument recognition. - Marcela Morvidone; Bob L. Sturm; Laurent Daudet (Pattern Recognit. Lett., 2010)

Sparse Approximation and the Pursuit of Meaningful Signal Models With Interference Adaptation. - Bob L. Sturm; John J. Shynk (IEEE Trans. Speech Audio Process., 2010)

A perceptually reweighted mixed-norm method for sparse approximation of audio signals. - Mads Græsbøll Christensen; Bob L. Sturm (ACSCC, 2011)

Cyclic pure greedy algorithms for recovering compressively sampled sparse signals. - Bob L. Sturm; Mads Græsbøll Christensen; Rémi Gribonval (ACSCC, 2011)

Sound Cross-synthesis and Morphing Using Dictionary-based Methods. - Nick Collins; Bob L. Sturm (ICMC, 2011)

A Study on Sparse Vector Distributions and Recovery from Compressed Sensing - Bob L. Sturm (CoRR, 2011)

Recursive nearest neighbor search in a sparse and multiscale domain for comparing audio signals. - Bob L. Sturm; Laurent Daudet (Signal Process., 2011)

A Survey of Evaluation in Music Genre Recognition. - Bob L. Sturm (Adaptive Multimedia Retrieval, 2012)

Regression with sparse approximations of data. - Pardis Noorzad; Bob L. Sturm (EUSIPCO, 2012)

When "exact recovery" is exact recovery in compressed sensing simulation. - Bob L. Sturm (EUSIPCO, 2012)

Comparison of orthogonal matching pursuit implementations. - Bob L. Sturm; Mads Græsbøll Christensen (EUSIPCO, 2012)

An analysis of the GTZAN music genre dataset. - Bob L. Sturm (MIRUM, 2012)

Two systems for automatic music genre recognition: what are they really recognizing? - Bob L. Sturm (MIRUM, 2012)

Making Explicit the Formalism Underlying Evaluation in Music Information Retrieval Research: A Look at the MIREX Automatic Mood Classification Task. - Bob L. Sturm (CMMR, 2013)

Behavior of greedy sparse representation algorithms on nested supports. - Boris Mailhé; Bob L. Sturm; Mark D. Plumbley (ICASSP, 2013)

On music genre classification via compressive sampling. - Bob L. Sturm (ICME, 2013)

Music genre recognition with risk and rejection. - Bob L. Sturm (ICME, 2013)

Evaluating music emotion recognition: Lessons from music genre recognition? - Bob L. Sturm (ICME Workshops, 2013)

Alexander Lerch: An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics. - Bob L. Sturm (Comput. Music. J., 2013)

The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use. - Bob L. Sturm (CoRR, 2013)

Classification accuracy is not enough - On the evaluation of music genre recognition systems. - Bob L. Sturm (J. Intell. Inf. Syst., 2013)

Revisiting Inter-Genre Similarity. - Bob L. Sturm; Fabien Gouyon (IEEE Signal Process. Lett., 2013)

On Theorem 10 in "On Polar Polytopes and the Recovery of Sparse Representations" [Sep 07 3188-3195]. - Bob L. Sturm; Boris Mailhé; Mark D. Plumbley (IEEE Trans. Inf. Theory, 2013)

A closer look at deep learning neural networks with low-level spectral periodicity features. - Bob L. Sturm; Corey Kereliuk; Aggelos Pikrakis (CIP, 2014)

Sound and Music Computing at Aalborg University in Copenhagen. - Stefania Serafin; Sofia Dahl; Amalia de Götzen; Cumhur Erkut; Dan Overholt; Hendrik Purwins; Bob L. Sturm (ICMC, 2014)

Formalizing the Problem of Music Description. - Bob L. Sturm; Rolf Bardeli; Thibault Langlois; Valentin Emiya (ISMIR, 2014)

The Kiki-Bouba Challenge: Algorithmic Composition for Content-based MIR Research and Development. - Bob L. Sturm; Nick Collins (ISMIR, 2014)

On Evaluation Validity in Music Autotagging. - Fabien Gouyon; Bob L. Sturm; João Lobato Oliveira; Nuno Hespanhol; Thibault Langlois (CoRR, 2014)

A Simple Method to Determine if a Music Information Retrieval System is a "Horse". - Bob L. Sturm (IEEE Trans. Multim., 2014)

¿El Caballo Viejo? Latin Genre Recognition with Deep Learning and Spectral Periodicity. - Bob L. Sturm; Corey Kereliuk; Jan Larsen (MCM, 2015)

Deep learning, audio adversaries, and music content analysis. - Corey Kereliuk; Bob L. Sturm; Jan Larsen (WASPAA, 2015)

Deep Learning and Music Adversaries. - Corey Kereliuk; Bob L. Sturm; Jan Larsen (IEEE Trans. Multim., 2015)

The "Beyond the Fence" Musical and "Computer Says Show" Documentary. - 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; et al. (ICCC, 2016)

Analysing Scattering-Based Music Content Analysis Systems: Where's the Music?. - Francisco Rodríguez-Algarra; Bob L. Sturm; Hugo Maruri-Aguilar (ISMIR, 2016)

Revisiting Priorities: Improving MIR Evaluation Practices. - Bob L. Sturm (ISMIR, 2016)

The "Horse" Inside: Seeking Causes Behind the Behaviors of Music Content Analysis Systems. - Bob L. Sturm (Comput. Entertain., 2016)

The "Horse" Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems. - Bob L. Sturm (CoRR, 2016)

Music transcription modelling and composition using deep learning. - Bob L. Sturm; João Felipe Santos; Oded Ben-Tal; Iryna Korshunova (CoRR, 2016)

Exploring Trends in Trinidad Steelband Music Through Computational Ethnomusicology. - Elio Quinton; Florabelle Spielmann; Bob L. Sturm (CMMR, 2017)

Local Interpretable Model-Agnostic Explanations for Music Content Analysis. - Saumitra Mishra; Bob L. Sturm; Simon Dixon (ISMIR, 2017)

"What are You Listening to?" Explaining Predictions of Deep Machine Listening Systems. - Saumitra Mishra; Bob L. Sturm; Simon Dixon (EUSIPCO, 2018)

A Deeper Look at Gaussian Mixture Model Based Anti-Spoofing Systems. - Bhusan Chettri; Bob L. Sturm (ICASSP, 2018)

Understanding a Deep Machine Listening Model Through Feature Inversion. - Saumitra Mishra; Bob L. Sturm; Simon Dixon (ISMIR, 2018)

Analysing Replay Spoofing Countermeasure Performance under varied conditions. - Bhusan Chettri; Bob L. Sturm; Emmanouil Benetos (MLSP, 2018)

Analysing The Predictions Of a CNN-Based Replay Spoofing Detection System. - Bhusan Chettri; Saumitra Mishra; Bob L. Sturm; Emmanouil Benetos (SLT, 2018)

A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing. - Bhusan Chettri; Saumitra Mishra; Bob L. Sturm; Emmanouil Benetos (CoRR, 2018)

Ethical Dimensions of Music Information Retrieval Technology. - Andre Holzapfel; Bob L. Sturm; Mark Coeckelbergh (Trans. Int. Soc. Music. Inf. Retr., 2018)

Ensemble Models for Spoofing Detection in Automatic Speaker Verification. - Bhusan Chettri; Daniel Stoller; Veronica Morfi; Marco A. Martínez Ramírez; Emmanouil Benetos; Bob L. Sturm (INTERSPEECH, 2019)

GAN-based Generation and Automatic Selection of Explanations for Neural Networks. - Saumitra Mishra; Daniel Stoller; Emmanouil Benetos; Bob L. Sturm; Simon Dixon (CoRR, 2019)

Introduction to the Issue on Data Science: Machine Learning for Audio Signal Processing. - Hendrik Purwins; Bob L. Sturm; Bo Li 028; Juhan Nam; Abeer Alwan (IEEE J. Sel. Top. Signal Process., 2019)

Characterising Confounding Effects in Music Classification Experiments through Interventions. - Francisco Rodríguez-Algarra; Bob L. Sturm; Simon Dixon (Trans. Int. Soc. Music. Inf. Retr., 2019)

Reliable Local Explanations for Machine Listening. - Saumitra Mishra; Emmanouil Benetos; Bob L. Sturm; Simon Dixon (IJCNN, 2020)

Dataset Artefacts in Anti-Spoofing Systems: A Case Study on the ASVspoof 2017 Benchmark. - Bhusan Chettri; Emmanouil Benetos; Bob L. T. Sturm (IEEE ACM Trans. Audio Speech Lang. Process., 2020)

Speech endpoint annotations and artefact details for ASVspoof 2017 version 2.0 dataset. - Bhusan Chettri; Emmanouil Benetos; Bob L. Sturm (Zenodo, 2020)

De-centering the West: East Asian Philosophies and the Ethics of Applying Artificial Intelligence to Music. - Rujing Stacy Huang; Bob L. T. Sturm; Andre Holzapfel (ISMIR, 2021)

Investigating the Relationship Between Liking and Belief in AI Authorship in the Context of Irish Traditional Music. - Ken Déguernel; Bob L. T. Sturm; Hugo Maruri-Aguilar (CREAI@AI*IA, 2022)

Tradformer: A Transformer Model of Traditional Music Transcriptions. - Luca Casini; Bob L. T. Sturm (IJCAI, 2022)

Editors' Notes. - Ken Déguernel; Bob L. T. Sturm; Artemi-Maria Gioti; Georgina Born (Comput. Music. J., 2022)

Sound and Video Anthology: Program Notes. - Ken Déguernel; Bob L. T. Sturm; Artemi-Maria Gioti; Georgina Born (Comput. Music. J., 2022)

Sound Anthology: Content. - Ken Déguernel; Bob L. T. Sturm; Artemi-Maria Gioti; Georgina Born; Oded Ben-Tal; Joëlle Léandre; Aaron Einbond; Iván Paz; Nao Tokui (Comput. Music. J., 2022)

TimbreCLIP: Connecting Timbre to Text and Images. - Nicolas Jonason; Bob L. T. Sturm (CoRR, 2022)

Audio Latent Space Cartography. - Nicolas Jonason; Bob L. T. Sturm (CoRR, 2022)

Editorial for TISMIR Special Collection: AI and Musical Creativity. - Bob L. T. Sturm; Alexandra L. Uitdenbogerd; Hendrik Vincent Koops; Anna Huang (Trans. Int. Soc. Music. Inf. Retr., 2022)

Bias in Favour or Against Computational Creativity: A Survey and Reflection on the Importance of Socio-cultural Context in its Evaluation. - Ken Déguernel; Bob L. T. Sturm (ICCC, 2023)

A Review of Validity and Its Relationship to Music Information Research. - Bob L. T. Sturm; Arthur Flexer (ISMIR, 2023)

Validity in Music Information Research Experiments. - Bob L. T. Sturm; Arthur Flexer (CoRR, 2023)

Exploring Softly Masked Language Modelling for Controllable Symbolic Music Generation. - Nicolas Jonason; Bob L. T. Sturm (CoRR, 2023)

DDSP-based Neural Waveform Synthesis of Polyphonic Guitar Performance from String-wise MIDI Input. - Nicolas Jonason; Xin Wang 037; Erica Cooper; Lauri Juvela; Bob L. T. Sturm; Junichi Yamagishi (CoRR, 2023)

Retrieval Augmented Generation of Symbolic Music with LLMs. - Nicolas Jonason; Luca Casini; Carl Thomé; Bob L. T. Sturm (CoRR, 2023)

Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music. - Rujing Stacy Huang; Andre Holzapfel; Bob L. T. Sturm; Anna-Kaisa Kaila (Trans. Int. Soc. Music. Inf. Retr., 2023)

Investigating the Viability of Masked Language Modeling for Symbolic Music Generation in abc-notation. - Luca Casini; Nicolas Jonason; Bob L. T. Sturm (EvoMUSART, 2024)

The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies. - David Dalmazzo; Ken Déguernel; Bob L. T. Sturm (EvoMUSART, 2024)

A Critical Survey of Research in Music Genre Recognition. - Owen Green; Bob L. T. Sturm; Georgina Born; Melanie Wald-Fuhrmann (ISMIR, 2024)

Negotiating Autonomy and Trust when Performing with an AI Musician. - Steve Benford; Marco Amerotti; Bob L. Sturm; Juan Pablo Martinez-Avila (TAS, 2024)

Reflections on The First International Conference in AI Music Studies. - Bob L. T. Sturm; Kanhov Elin; André Holzapfel; Oded Ben-Tal (Comput. Music. J., 2024)

SYMPLEX: Controllable Symbolic Music Generation using Simplex Diffusion with Vocabulary Priors. - Nicolas Jonason; Luca Casini; Bob L. T. Sturm (CoRR, 2024)

Steer-by-prior Editing of Symbolic Music Loops. - Nicolas Jonason; Luca Casini; Bob L. T. Sturm (CoRR, 2024)

Introducing the TISMIR Education Track: What, Why, How? - Meinard Müller; Simon Dixon; Anja Volk; Bob L. T. Sturm; Preeti Rao; Mark Gotham (Trans. Int. Soc. Music. Inf. Retr., 2024)

(Mis)Communicating with our AI Systems. - Laura Cros Vila; Bob L. T. Sturm (CHI, 2025)

Exploring the Expressive Space of an Articulatory Vocal Modal using Quality-Diversity Optimization with Multimodal Embeddings. - Joris Grouwels; Nicolas Jonason; Bob L. T. Sturm (GECCO, 2025)

The AI Music Arms Race: On the Detection of AI-Generated Music. - Laura Cros Vila; Bob L. T. Sturm; Luca Casini; David Dalmazzo (Trans. Int. Soc. Music. Inf. Retr., 2025)

SMART: Tuning a symbolic music generation system with an audio domain aesthetic reward. - Nicolas Jonason; Luca Casini; Bob L. T. Sturm (CoRR, 2025)

"I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generation. - Bob L. T. Sturm (CoRR, 2025)