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Simple Search
Hits 1 – 9 of 9
1
Animal linguistics in the making: the Urgency Principle and titi monkeys’ alarm system
Narbona Sabaté, Lara
;
Mesbahi, Geoffrey
;
Dezecache, Guillaume
...
In: ISSN: 0394-9370 ; Ethology Ecology and Evolution ; https://hal.inrae.fr/hal-03518874 ; Ethology Ecology and Evolution, Taylor & Francis, 2022, pp.1-17. ⟨10.1080/03949370.2021.2015452⟩ (2022)
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2
The lek breeding system of the Kākāpō (Strigops habroptilus): the role of vocalisations in female mate choice and kin clustering on leks
Kelman, Emma
. - : University of Otago, 2018
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3
I thought that I heard you laughing: Contextual facial expressions modulate the perception of authentic laughter and crying.
Lavan, N
;
Lima, CF
;
Harvey, H
...
In: Cogn Emot , 29 (5) pp. 935-944. (2015) (2015)
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4
Synthesis of listener vocalisations with imposed intonation contours
Sathish Pammi
;
Marcela Charfuelan
In: http://www.dfki.de/~schroed/articles/pammi_etal2010a.pdf
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5
AUDIOVISUAL VOCAL OUTBURST RECOGNITION IN NOISY ACOUSTIC CONDITIONS
Florian Eyben
;
Stavros Petridis
;
Björn Schuller
...
In: http://www.mmk.ei.tum.de/publ//pdf/12/12eyb1.pdf
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6
Audiovisual classification of vocal outburst in human conversation using long–short-term memory networks
Florian Eyben
;
Stavros Petridis
;
Björn Schuller
...
In: http://ibug.doc.ic.ac.uk/media/uploads/documents/icassp-2011-eybenetal-final.pdf
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7
AUDIOVISUAL CLASSIFICATION OF VOCAL OUTBURSTS IN HUMAN CONVERSATION USING LONG-SHORT-TERM MEMORY NETWORKS
Florian Eyben
;
Stavros Petridis
;
Björn Schuller
...
In: http://www.mmk.ei.tum.de/publ/pdf/11/11eyb2.pdf
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8
AUDIOVISUAL VOCAL OUTBURST CLASSIFICATION IN NOISY ACOUSTIC CONDITIONS
Florian Eyben
;
Stavros Petridis
;
Björn Schuller
;
Maja Pantic
In: http://ibug.doc.ic.ac.uk/media/uploads/documents/eybenetal_icassp2012.pdf
Abstract:
In this study, we investigate an audiovisual approach for classification of vocal outbursts (non-linguistic vocalisations) in noisy conditions using Long Short-Term Memory (LSTM) Recurrent Neural Networks and Support Vector Machines. Fusion of geometric shape features and acoustic low-level descriptors is performed on the feature level. Three different types of acoustic noise are considered: babble, office and street noise. Experiments are conducted on every noise type to asses the benefit of the fusion in each case. As database for evaluations serves the INTERSPEECH 2010 Paralinguistic Challenge’s Audiovisual Interest Corpus of human-to-human natural conversation. The results show that even when training is performed on noise corrupted audio which matches the test conditions the addition of visual features is still beneficial.
Keyword:
Audiovisual Processing
;
Index Terms — Non-linguistic Vocalisations
;
Laughter
;
Long Short-Term Memory
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.371.4045
http://ibug.doc.ic.ac.uk/media/uploads/documents/eybenetal_icassp2012.pdf
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9
AUDIOVISUAL DETECTION OF LAUGHTER IN HUMAN-MACHINE INTERACTION
Stavros Petridis
;
Maelle Leveque
;
Maja Pantic
In: http://ibug.doc.ic.ac.uk/media/uploads/documents/petridislevequepantic_acii2013.pdf
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