Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Bob L. Sturm"'
Publikováno v:
Transactions of the International Society for Music Information Retrieval, Vol 1, Iss 1, Pp 44-55 (2018)
This article examines ethical dimensions of Music Information Retrieval (MIR) technology. It uses practical ethics (especially computer ethics and engineering ethics) and socio-technical approaches to provide a theoretical basis that can inform discu
Externí odkaz:
https://doaj.org/article/3cb4e1e31a24410fb97616fe0223e1f2
Publikováno v:
Transactions of the International Society for Music Information Retrieval, Vol 2, Iss 1 (2019)
We address the problem of confounding in the design of music classification experiments, that is, the inability to distinguish the effects of multiple potential influencing variables in the measurements. Confounding affects the validity of conclusion
Externí odkaz:
https://doaj.org/article/cac31a26cc9141a3ab5de2729054bc24
Autor:
Bob L. Sturm, Oded Ben-Tal
Publikováno v:
Journal of Creative Music Systems, Vol 2, Iss 1 (2017)
We extend our evaluation of generative models of music transcriptions that were first presented in Sturm, Santos, Ben-Tal, and Korshunova (2016). We evaluate the models in five different ways: 1) at the population level, comparing statistics of 30,00
Externí odkaz:
https://doaj.org/article/ec351c88ec3245ea8ec879a89afb7de3
Autor:
Berkowitz, Adam Eric1 aeberkowitz@crimson.ua.edu
Publikováno v:
Information Technology & Libraries. Sep2024, Vol. 43 Issue 3, p1-15. 15p.
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 28:3018-3028
The Automatic Speaker Verification Spoofing and Countermeasures Challenges motivate research in protecting speech biometric systems against a variety of different access attacks. The 2017 edition focused on replay spoofing attacks, and involved parti
Critical but often overlooked research questions in artificial intelligence (AI) applied to music involve the impact of the results for music. How and to what extent does such research contribute to the domain of music? How are the resulting models u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::030223fa75250ca80cf2c954dda7028c
https://eprints.kingston.ac.uk/id/eprint/47015/1/Ben-Tal-O-47015-AAM.pdf
https://eprints.kingston.ac.uk/id/eprint/47015/1/Ben-Tal-O-47015-AAM.pdf
Autor:
Bob L. Sturm, Oded Ben-Tal
Publikováno v:
Handbook of Artificial Intelligence for Music ISBN: 9783030721152
This chapter motivates the application of Artificial Intelligence (AI) to modeling styles of folk music. In this context, we focus particularly on questions about the meaningful evaluation of such AI, and argue that music practitioners should be inte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9dbb01f24e9a5ddab374eed9a4e17125
https://doi.org/10.1007/978-3-030-72116-9_16
https://doi.org/10.1007/978-3-030-72116-9_16
Publikováno v:
IJCNN
One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions. Sensitivity analysis, which involves analysing the effect of input perturbations on mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a6654a857fc53903b25806510344446
http://arxiv.org/abs/2005.07788
http://arxiv.org/abs/2005.07788
Autor:
Úna Monaghan, Bob L. Sturm, Oded Ben-Tal, François Pachet, Emmanuel Deruty, Gaëtan Hadjeres, Nick Collins, Dorien Herremans, Elaine Chew
Publikováno v:
Journal of New Music Research
Journal of New Music Research, Taylor & Francis (Routledge), 2019, 48 (1), pp.36-55. ⟨10.1080/09298215.2018.1515233⟩
Journal of New Music Research, Taylor & Francis (Routledge), 2019, 48 (1), pp.36-55. ⟨10.1080/09298215.2018.1515233⟩
International audience; Research applying machine learning to music modeling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 13:203-205
The papers in this special section focus on audio signal processing which is currently undergoing a paradigm change, where data-driven machine learning is replacing hand-crafted feature design. These papers aim to promote progress, systematization, u