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LONG-DISTANCE DETECTION OF BIOACOUSTIC EVENTS WITH PER-CHANNEL ENERGY NORMALIZATION

Lostanlen, V; Palmer, K; Knight, E; Clark, C; Klinck, H; Farnsworth, A; Wong, T; Cramer, J; Bello, JP (October 2019, Detection and Classification of Acoustic Scenes and Events 2019)

This paper proposes to perform unsupervised detection of bioacous- tic events by pooling the magnitudes of spectrogram frames after per-channel energy normalization (PCEN). Although PCEN was originally developed for speech recognition, it also has beneficial effects in enhancing animal vocalizations, despite the presence of atmospheric absorption and intermittent noise. We prove that PCEN generalizes logarithm-based spectral flux, yet with a tunable time scale for background noise estimation. In comparison with point- wise logarithm, PCEN reduces false alarm rate by 50x in the near field and 5x in the far field, both on avian and marine bioacoustic datasets. Such improvements come at moderate computational cost and require no human intervention, thus heralding a promising future for PCEN in bioacoustics.
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SONYC URBAN SOUND TAGGING (SONYC-UST): A MULTILABEL DATASET FROM AN URBAN ACOUSTIC SENSOR NETWORK

Cartwright, M.; Mendez, A.; Cramer, J.; Lostanlen, V.; Dove, G.; Wu, H.; Salamon, J.; Nov, O.; Bello, J. P. (October 2019, Detection and Classification of Acoustic Scenes and Events 2019)

SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and evaluation of machine listening systems for realworld urban noise monitoring. It consists of 3068 audio recordings from the “Sounds of New York City” (SONYC) acoustic sensor network. Via the Zooniverse citizen science platform, volunteers tagged the presence of 23 fine-grained classes that were chosen in consultation with the New York City Department of Environmental Protection. These 23 fine-grained classes can be grouped into eight coarse-grained classes. In this work, we describe the collection of this dataset, metrics used to evaluate tagging systems, and the results of a simple baseline model.
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Accelerated Experimental Design for Pairwise Comparisons

https://doi.org/10.1137/1.9781611975673.49

Guo, Y.; Dy, J.; Erdogmus, D.; Kalpathy-Cramer, J.; Ostmo, S.; Campbell, J.P.; Chiang, M.F.; Ioannidis, S. (May 2019, Proceedings of the 2019 SIAM International Conference on Data Mining)

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Erratum: Strange and multistrange particle production in Au+Au collisions at $\sqrt{s_{N N}} = 62.4$ GeV [Phys. Rev. C 83 , 024901 (2011)]

https://doi.org/10.1103/PhysRevC.107.049903

Aggarwal, M. M.; Ahammed, Z.; Alakhverdyants, A. V.; Alekseev, I.; Alford, J.; Anderson, B. D.; Anson, C. D.; Arkhipkin, D.; Averichev, G. S.; Balewski, J.; et al (April 2023, Physical Review C)

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Erratum: Azimuthal Anisotropy of $K_{S}^{0}$ and $Λ + \bar{Λ}$ Production at Midrapidity from $Au + Au$ Collisions at ${\sqrt{s}}_{N N} = 130 GeV$ [Phys. Rev. Lett. 89 , 132301 (2002)]

https://doi.org/10.1103/PhysRevLett.127.089901

Adler, C.; Ahammed, Z.; Allgower, C.; Amonett, J.; Anderson, B. D.; Anderson, M.; Averichev, G. S.; Balewski, J.; Barannikova, O.; Barnby, L. S.; et al (August 2021, Physical Review Letters)

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Erratum: Azimuthal Anisotropy at the Relativistic Heavy Ion Collider: The First and Fourth Harmonics [Phys. Rev. Lett. 92 , 062301 (2004)]

https://doi.org/10.1103/PhysRevLett.127.069901

Adams, J.; Adler, C.; Aggarwal, M. M.; Ahammed, Z.; Amonett, J.; Anderson, B. D.; Anderson, M.; Arkhipkin, D.; Averichev, G. S.; Badyal, S. K.; et al (August 2021, Physical Review Letters)

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Erratum: Observation of $D^{0}$ Meson Nuclear Modifications in $Au + Au$ Collisions at $\sqrt{s_{NN}} = 200 GeV$ [Phys. Rev. Lett. 113 , 142301 (2014)]

https://doi.org/10.1103/PhysRevLett.121.229901

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Alekseev, I.; Alford, J.; Anson, C. D.; Aparin, A.; Arkhipkin, D.; et al (November 2018, Physical Review Letters)

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