Baboon loud calls advertise male quality: acoustic features and their relation to rank, age, and exhaustion

2004 ◽  
Vol 56 (2) ◽  
pp. 140-148 ◽  
Author(s):  
Julia Fischer ◽  
Dawn M. Kitchen ◽  
Robert M. Seyfarth ◽  
Dorothy L. Cheney
Keyword(s):  
Male Quality ◽  
Acoustic Features ◽  
Loud Calls

Acoustic features of male baboon loud calls: Influences of context, age, and individuality

2002 ◽  
Vol 111 (3) ◽  
pp. 1465-1474 ◽  
Author(s):  
Julia Fischer ◽  
Kurt Hammerschmidt ◽  
Dorothy L. Cheney ◽  
Robert M. Seyfarth
Keyword(s):  
Acoustic Features ◽  
Loud Calls

Acoustic and Temporal Variation in Gelada (Theropithecus gelada) Loud Calls Advertise Male Quality

2016 ◽  
Vol 37 (4-5) ◽  
pp. 568-585 ◽  
Author(s):  
Marcela E. Benítez ◽  
Aliza le Roux ◽  
Julia Fischer ◽  
Jacinta C. Beehner ◽  
Thore J. Bergman
Keyword(s):  
Temporal Variation ◽  
Male Quality ◽  
Theropithecus Gelada ◽  
Loud Calls

Communication of Male Quality in Owl Hoots

2007 ◽  
Vol 169 (4) ◽  
pp. 552
Author(s):  
Loïc A. Hardouin ◽  
Reby ◽  
Bavoux ◽  
Burneleau ◽  
Bretagnolle
Keyword(s):  
Male Quality

scholarly journals Categorizing Touch-Input Locations from Touchscreen Device Interfaces via On-Board Mechano-Acoustic Transducers

2021 ◽  
Vol 11 (11) ◽  
pp. 4834
Author(s):  
Kai Ren Teo ◽  
Balamurali B T ◽  
Jianying Zhou ◽  
Jer-Ming Chen
Keyword(s):  
User Interaction ◽  
Data Access ◽  
Machine Learning Techniques ◽  
Acoustic Features ◽  
Touch Input ◽  
Acoustic Transducers ◽  
Learning Techniques ◽  
Access Controls ◽  
Input Location ◽  
Mitigation Methods

Many mobile electronics devices, including smartphones and tablets, require the user to interact physically with the device via tapping the touchscreen. Conveniently, these compact devices are also equipped with high-precision transducers such as accelerometers and microphones, integrated mechanically and designed on-board to support a range of user functionalities. However, unintended access to these transducer signals (bypassing normal on-board data access controls) may allow sensitive user interaction information to be detected and thereby exploited. In this study, we show that acoustic features extracted from the on-board microphone signals, supported with accelerometer and gyroscope signals, may be used together with machine learning techniques to successfully determine the user’s touch input location on a touchscreen: our ensemble model, namely the random forest model, predicts touch input location with up to 86% accuracy in a realistic scenario. Accordingly, we present the approach and techniques used, the performance of the model developed, and also discuss limitations and possible mitigation methods to thwart possible exploitation of such unintended signal channels.

scholarly journals Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

2021 ◽  
Vol 13 (4) ◽  
pp. 628
Author(s):  
Liang Ye ◽  
Tong Liu ◽  
Tian Han ◽  
Hany Ferdinando ◽  
Tapio Seppänen ◽  
...  
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Role Playing ◽  
School Bullying ◽  
Image Features ◽  
Campus Violence ◽  
Surveillance Video ◽  
Acoustic Features ◽  
Mel Frequency Cepstral Coefficients ◽  
Violence Detection

Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.

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