Publikationer av Bobby Lee Townsend Sturm JR
Refereegranskade
Artiklar
[1]
R. Huang et al., "Beyond Diverse Datasets : Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music," Transactions of the International Society for Music Information Retrieval, vol. 6, no. 1, s. 43-59, 2023.
[2]
O. Ben-Tal, M. Harris och B. Sturm, "How music AI is useful : Engagements with composers, performers, and audiences," Leonardo music journal, vol. 54, no. 5, s. 510-516, 2021.
[3]
B. Sturm och H. Maruri-Aguilar, "The Ai Music Generation Challenge 2020: Double Jigs in the Style of O'Neill's ``1001''," Journal of Creative Music Systems, vol. 5, no. 1, 2021.
[4]
B. Chettri, E. Benetos och B. Sturm, "Dataset Artefacts in Anti-Spoofing Systems : A Case Study on the ASVspoof 2017 Benchmark," IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol. 28, s. 3018-3028, 2020.
[5]
B. Sturm et al., "Artificial Intelligence and Music: Open Questions of Copyright Law and Engineering Praxis," MDPI Arts, vol. 8, no. 3, 2019.
[6]
F. Rodríguez-Algarra, B. Sturm och S. Dixon, "Characterising Confounding Effects in Music Classification Experiments through Interventions," Transactions of the International Society for Music Information Retrieval, vol. 2, no. 1, s. 52-66, 2019.
[7]
H. Purwins et al., "Introduction to the Issue on Data Science : Machine Learning for Audio Signal Processing," IEEE Journal on Selected Topics in Signal Processing, vol. 13, no. 2, s. 203-205, 2019.
[8]
B. Sturm et al., "Machine Learning Research that Matters for Music Creation : A Case Study," Journal of New Music Research, vol. 48, no. 1, s. 36-55, 2019.
[9]
A. Holzapfel, B. Sturm och M. Coeckelbergh, "Ethical Dimensions of Music Information Retrieval Technology," Transactions of the International Society for Music Information Retrieval, vol. 1, no. 1, s. 44-55, 2018.
Konferensbidrag
[10]
M. Amerotti et al., "A Live Performance Rule System Informed by Irish Traditional Dance Music," i Proc. International Symposium on Computer Music Multidisciplinary Research, 2023.
[11]
B. Sturm och A. Flexer, "A Review of Validity and its Relationship to Music Information Research," i Proc. Int. Symp. Music Information Retrieval, 2023.
[12]
S. Falk, B. Sturm och S. Ahlbäck, "Automatic legato transcription based on onset detection," i SMC 2023 : Proceedings of the Sound and Music Computing Conference 2023, 2023, s. 214-221.
[13]
K. Déguernel och B. Sturm, "Bias in Favour or Against Computational Creativity : A Survey and Reflection on the Importance of Socio-cultural Context in its Evaluation," i Proc. International Conference on Computational Creativity, 2023.
[14]
L. Cros Vila och B. Sturm, "Statistical evaluation of abc-formatted music at the levels of items and corpora," i Proc. AI Music Creativity Conference, 2023.
[15]
B. Sturm, "The Ai Music Generation Challenge 2022 : Summary and Results," i Proc. AI Music Creativity Conference, 2023.
[16]
B. Sturm, "Generative AI helps one express things for which they may not have expressions (yet)," i Proc. Generative AI and HCI Workshop at CHI, 2022.
[17]
K. Déguernel, B. Sturm och H. Maruri-Aguilar, "Investigating the relationship between liking and belief in AI authorship in the context of Irish traditional music," i Workshop on Artificial Intelligence and Creativity, 2022.
[18]
N. Jonason och B. Sturm, "Neural music instrument cloning from few samples," i Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22), 2022, s. 296-303.
[19]
B. Sturm, "The Ai music generation challenge 2021 : Summary and results," i Proceedings of the 3rd Conference on AI Music Creativity, AIMC, 2022.
[20]
L. Casini och B. Sturm, "Tradformer : A Transformer Model of Traditional Music Transcriptions," i Proceedings 31st International Joint Conference on Artificial Intelligence, IJCAI 2022, 2022, s. 4915-4920.
[21]
B. Sturm, "An artificial critic of Irish double jigs," i Proceedings of the 2nd Joint Conference on AI Music Creativity, AIMC, 2021, s. 10.
[22]
R. Huang, B. Sturm och A. Holzapfel, "De-centering the west : East asian philosophies and the ethics of applying artificial intelligence to music," i International Society for Music Information Retrieval Conference, ISMIR, 2021.
[23]
R. Huang och B. Sturm, "Reframing “Aura” : Authenticity in the application of AI to Irish traditional music," i 2nd Conference on AI Music Creativity 2021, 2021.
[24]
S. Mishra et al., "Reliable Local Explanations for Machine Listening," i 2020 International Joint Conference on Neural Networks (IJCNN), 2020.
[25]
N. Jonason, B. Sturm och C. Thomé, "The control-synthesis approach for making expressive and controllable neural music synthesizers," i Proceedings of the 2020 AI Music Creativity Conference, 2020.
[26]
B. Chettri et al., "Ensemble models for spoofing detection in automatic speaker verification," i Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2019, 2019, s. 1018-1022.
[27]
O. Ben-Tal et al., "Finding Music in Music Data : A Summary of the DaCaRyH Project," i Computational phonogram archiving, 2019, s. 191-205.
[28]
E. Hallström et al., "From Jigs and Reels to Schottisar och Polskor : Generating Scandinavian-like Folk Music with Deep Recurrent Networks," i Proceedings of the Sound and Music Computing Conferences, 2019.
[29]
S. Mishra et al., "GAN-Based Generation and Automatic Selection of Explanations for Neural Networks," i Safe Machine Learning 2019 Workshop at the International Conference on Learning Representations, 2019.
[30]
E. Lousseief och B. Sturm, "MahlerNet : Unbounded Orchestral Music with Neural Networks," i Combined proceedings of the Nordic Sound and Music Computing Conference 2019 and the Interactive Sonification Workshop 2019, 2019, s. 57-63.
[31]
B. Chettri, B. Sturm och E. Benetos, "ANALYSING REPLAY SPOOFING COUNTERMEASURE PERFORMANCE UNDER VARIED CONDITIONS," i 2018 IEEE 28TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2018.
[32]
B. Chettri, S. Mishra och B. Sturm, "Analysing the predictions of a CNN-based replay spoofing detection system," i 2018 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2018), 2018, s. 92-97.
[33]
B. Sturm, "How Stuff Works: LSTM Model of Folk Music Transcriptions," i Joint Workshop on Machine Learning for Music, ICML, 2018.
[34]
B. Sturm, "What do these 5,599,881 parameters mean? : An analysis of a specific LSTM music transcription model, starting with the 70,281 parameters of its softmax layer," i Proceedings of the 6th International Workshop on Musical Metacreation (MUME 2018), 2018.
Kapitel i böcker
[35]
R. S. Huang, A. Holzapfel och B. Sturm, "Global Ethics : From Philosophy to Practice A Culturally Informed Ethics of Music AI in Asia," i Artificial Intelligence and Music Ecosystem, Martin Clancy red., : Routledge, 2022, s. 126-141.
[36]
B. Sturm och O. Ben-Tal, "Folk the Algorithms : (Mis)Applying Artificial Intelligence to Folk Music," i Handbook of Artificial Intelligence for Music, Switzerland : Springer Berlin/Heidelberg, 2021.
Icke refereegranskade
Artiklar
[37]
B. Sturm et al., "Editorial for TISMIR Special Collection : AI and Musical Creativity," Transactions of the International Society for Music Information Retrieval, vol. 5, no. 1, s. 67-70, 2022.
Rapporter
[38]
B. Sturm och O. Ben-Tal, "Let’s Have Another Gan Ainm : An experimental album of Irish traditional music and computer-generated tunes," , 2018.
Proceedings (redaktörskap)
[39]
"Proceedings of The 2020 Joint Conference on AI Music Creativity," , KTH Royal Institute of Technology, 2020.
Senaste synkning med DiVA:
2024-04-26 00:39:41