Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Justin Salamon"'
Publikováno v:
PLoS ONE, Vol 14, Iss 10, p e0214168 (2019)
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection
Externí odkaz:
https://doaj.org/article/bb59b6d77a7b42409f28e59cc4b022b7
Autor:
Justin Salamon, Juan Pablo Bello, Andrew Farnsworth, Matt Robbins, Sara Keen, Holger Klinck, Steve Kelling
Publikováno v:
PLoS ONE, Vol 11, Iss 11, p e0166866 (2016)
Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants' flight c
Externí odkaz:
https://doaj.org/article/9559a0ae37544543b1095f09b2ce1a57
Autor:
Vincent Lostanlen, Aurora Cramer, Justin Salamon, Andrew Farnsworth, Benjamin M. Van Doren, Steve Kelling, Juan Pablo Bello
The steady decline of avian populations worldwide urgently calls for a cyber-physical system to monitor bird migration at the continental scale. Compared to other sources of information (radar and crowdsourced observations), bioacoustic sensor networ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1eb078e757acf317f54e32756078bc75
https://doi.org/10.1101/2022.05.31.494155
https://doi.org/10.1101/2022.05.31.494155
Music segmentation algorithms identify the structure of a music recording by automatically dividing it into sections and determining which sections repeat and when. Since the desired granularity of the sections may vary by application, multi-level se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5c259395b0148dffb3e76c7951ce56f
Few-shot learning aims to train models that can recognize novel classes given just a handful of labeled examples, known as the support set. While the field has seen notable advances in recent years, they have often focused on multi-class image classi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f66c9288bc4986e4e92b5774f0e9411
http://arxiv.org/abs/2110.09600
http://arxiv.org/abs/2110.09600
Publikováno v:
ICASSP
Supervised learning for audio classification typically imposes a fixed class vocabulary, which can be limiting for real-world applications where the target class vocabulary is not known a priori or changes dynamically. In this work, we introduce a fe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4a7d69ff6f3c245faa16370fa03ebbc
https://doi.org/10.1109/icassp39728.2021.9413584
https://doi.org/10.1109/icassp39728.2021.9413584
Autor:
Charlie Mydlarz, Anish Arora, Cláudio T. Silva, Juan Pablo Bello, Justin Salamon, R. Luke DuBois, Oded Nov, Harish Doraiswamy
Publikováno v:
Communications of the ACM. 62:68-77
SONYC integrates sensors, machine listening, data analytics, and citizen science to address noise pollution in New York City.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67b142abe370cc8801506f2e49a072e1
https://doi.org/10.1145/3224204
https://doi.org/10.1145/3224204
Autor:
Mark Cartwright, Brian McFee, Juan Pablo Bello, Jong Wook Kim, Rachel M. Bittner, Justin Salamon
Publikováno v:
IEEE Signal Processing Magazine. 36:128-137
In the early years of music information retrieval (MIR), research problems were often centered around conceptually simple tasks, and methods were evaluated on small, idealized data sets. A canonical example of this is genre recognition-i.e., Which on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e574e285465f90f87f1a7b8148112a0
https://doi.org/10.1109/msp.2018.2875349
https://doi.org/10.1109/msp.2018.2875349
Autor:
Vincent Lostanlen, Andrew Farnsworth, Brian McFee, Steve Kelling, Juan Pablo Bello, Justin Salamon, Mark Cartwright
Publikováno v:
IEEE Signal Processing Letters. 26:39-43
In the context of automatic speech recognition and acoustic event detection, an adaptive procedure named per-channel energy normalization (PCEN) has recently shown to outperform the pointwise logarithm of mel-frequency spectrogram (logmelspec) as an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::283bc48c779561713206b8f271455baf
https://doi.org/10.1109/lsp.2018.2878620
https://doi.org/10.1109/lsp.2018.2878620
Autor:
Romain Serizel, Hakan Erdogan, Justin Salamon, Nicolas Turpault, John R. Hershey, Scott Wisdom, Eduardo Fonseca, Prem Seetharaman
Publikováno v:
ICASSP
ICASSP 2021-46th International Conference on Acoustics, Speech, and Signal Processing
ICASSP 2021-46th International Conference on Acoustics, Speech, and Signal Processing, Jun 2021, Toronto/Virtual, Canada. ⟨10.1109/ICASSP39728.2021.9414789⟩
ICASSP 2021-46th International Conference on Acoustics, Speech, and Signal Processing
ICASSP 2021-46th International Conference on Acoustics, Speech, and Signal Processing, Jun 2021, Toronto/Virtual, Canada. ⟨10.1109/ICASSP39728.2021.9414789⟩
International audience; We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We analyze the performance of the submissions to DCAS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd9b83c1e445912a1a4566dae921c839
https://hal.inria.fr/hal-02984675/document
https://hal.inria.fr/hal-02984675/document