Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Morfi, Veronica"'
Autor:
Nolasco, Inês, Singh, Shubhr, Morfi, Veronica, Lostanlen, Vincent, Strandburg-Peshkin, Ariana, Vidaña-Vila, Ester, Gill, Lisa, Pamuła, Hanna, Whitehead, Helen, Kiskin, Ivan, Jensen, Frants H., Morford, Joe, Emmerson, Michael G., Versace, Elisabetta, Grout, Emily, Liu, Haohe, Stowell, Dan
Automatic detection and classification of animal sounds has many applications in biodiversity monitoring and animal behaviour. In the past twenty years, the volume of digitised wildlife sound available has massively increased, and automatic classific
Externí odkaz:
http://arxiv.org/abs/2305.13210
Autor:
Zandberg, Lies1,2 (AUTHOR) elisabeth.zandberg@rhul.ac.uk, Morfi, Veronica3 (AUTHOR), George, Julia M.2,4 (AUTHOR), Clayton, David F.2,5 (AUTHOR), Stowell, Dan3,6,7 (AUTHOR), Lachlan, Robert F.1,2 (AUTHOR)
Publikováno v:
PLoS Computational Biology. 8/7/2024, Vol. 20 Issue 8, p1-21. 21p.
Sound scene geotagging is a new topic of research which has evolved from acoustic scene classification. It is motivated by the idea of audio surveillance. Not content with only describing a scene in a recording, a machine which can locate where the r
Externí odkaz:
http://arxiv.org/abs/2110.04585
Autor:
Chettri, Bhusan, Stoller, Daniel, Morfi, Veronica, Ramírez, Marco A. Martínez, Benetos, Emmanouil, Sturm, Bob L.
Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modeling approach. For robustness, we use both deep neural networks and traditional machine learning models and combine them as
Externí odkaz:
http://arxiv.org/abs/1904.04589
Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings c
Externí odkaz:
http://arxiv.org/abs/1811.02275
Data-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning
Autor:
Morfi, Veronica, Stowell, Dan
We propose a method to perform audio event detection under the common constraint that only limited training data are available. In training a deep learning system to perform audio event detection, two practical problems arise. Firstly, most datasets
Externí odkaz:
http://arxiv.org/abs/1807.06972
Autor:
Morfi, Veronica, Stowell, Dan
In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Sec
Externí odkaz:
http://arxiv.org/abs/1807.03697
Autor:
Morfi, Veronica, Stowell, Dan
Many approaches have been used in bird species classification from their sound in order to provide labels for the whole of a recording. However, a more precise classification of each bird vocalization would be of great importance to the use and manag
Externí odkaz:
http://arxiv.org/abs/1603.07173
Bird calls range from simple tones to rich dynamic multi-harmonic structures. The more complex calls are very poorly understood at present, such as those of the scientifically important corvid family (jackdaws, crows, ravens, etc.). Individual birds
Externí odkaz:
http://arxiv.org/abs/1603.07236
Rhythm quantisation is an essential part of converting performance MIDI recordings into musical scores. Previous works on rhythm quantisation are limited to the use of probabilistic or statistical methods. In this paper, we propose a MIDI-to-score qu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39f56dd948432d718627dad6b286de78