Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)

2021 ◽  
Author(s):  
Reyhaneh Abbasi ◽  
Peter Balazs ◽  
Maria Adelaide Marconi ◽  
Doris Nicolakis ◽  
Sarah M. Zala ◽  
...  
Keyword(s):  
Ground Truth ◽  
Ultrasonic Vocalizations ◽  
Detection Methods ◽  
High Signal ◽  
Systematic Comparison ◽  
New Class ◽  
Advantages And Disadvantages ◽  
Detection Failure ◽  
Human Hearing

House mice communicate through ultrasonic vocalizations (USVs), which are above the range of human hearing (>20 kHz), and several automated methods have been developed for USV detection and classification. Here we evaluate their advantages and disadvantages in a full, systematic comparison. We compared the performance of four detection methods, DeepSqueak (DSQ), MUPET, USVSEG, and the Automatic Mouse Ultrasound Detector (A-MUD). Moreover, we compared these to human-based manual detection (considered as ground truth), and evaluated the inter-observer reliability. All four methods had comparable rates of detection failure, though A-MUD outperformed the others in terms of true positive rates for recordings with low or high signal-to-noise ratios. We also did a systematic comparison of existing classification algorithms, where we found the need to develop a new method for automating the classification of USVs using supervised classification, bootstrapping on Gammatone Spectrograms, and Convolutional Neural Networks algorithms with Snapshot ensemble learning (BootSnap). It successfully classified calls into 12 types, including a new class of false positives used for detection refinement. BootSnap provides enhanced performance compared to state-of-the-art tools, it has an improved generalizability, and it is freely available for scientific use.

scholarly journals Analysis of Mouse Vocal Communication (AMVOC): A deep, unsupervised method for rapid detection, analysis, and classification of ultrasonic vocalizations

2021 ◽  
Author(s):  
Vasiliki Stoumpou ◽  
César D M Vargas ◽  
Peter F Schade ◽  
Theodoros Giannakopoulos ◽  
Erich D Jarvis
Keyword(s):  
Vocal Communication ◽  
Ground Truth ◽  
Ultrasonic Vocalizations ◽  
Experimental Conditions ◽  
Detection Analysis ◽  
Latent Features ◽  
Convolutional Autoencoder ◽  
Unsupervised Deep Learning ◽  
Experimental Use

Some aspects of the neural mechanisms underlying mouse ultrasonic vocalizations (USVs) are a useful model for the neurobiology of human speech and speech-related disorders. Much of the research on vocalizations and USVs is limited to offline methods and supervised classification of USVs, hindering the discovery of new types of vocalizations and the study of real-time free behavior. To address these issues, we developed AMVOC (Analysis of Mouse VOcal Communication) as a free, open-source software to analyze and detect USVs in both online and offline modes. When compared to hand-annotated ground-truth USV data, AMVOC's detection functionality (both offline and online) has high accuracy, and outperforms leading methods in noisy conditions, thus allowing for broader experimental use. AMVOC also includes the implementation of an unsupervised deep learning approach that facilitates discovery and analysis of USV data by clustering USVs using latent features extracted by a convolutional autoencoder and isimplemented in a graphical user interface (GUI), also enabling user's evaluation. These results can be used to explore the vocal repertoire space of the analyzed vocalizations. In this way, AMVOC will facilitate vocal analyses in a broader range of experimental conditions and allow users to develop previously inaccessible experimental designs for the study of mouse vocal behavior.

scholarly journals AUTOMATIC CLASSIFICATION OF TREES FROM LASER SCANNING POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 137-144 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Detection Methods ◽  
Local Maxima ◽  
Advantages And Disadvantages ◽  
Close Proximity ◽  
Point Density ◽  
Grid Surface

Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatically classifying laser scanning point clouds into ’tree’ and ’non-tree’ classes. Our method uses the 3D coordinates of the laser scanning points as input and generates a new point cloud which holds a label for each point indicating if it belongs to the ’tree’ or ’non-tree’ class. To do so, a grid surface is assigned to the lowest height level of the point cloud. The grids are filled with probability values which are calculated by checking the point density above the grid. Since the tree trunk locations appear with very high values in the probability matrix, selecting the local maxima of the grid surface help to detect the tree trunks. Further points are assigned to tree trunks if they appear in the close proximity of trunks. Since heavy mathematical computations (such as point cloud organization, detailed shape 3D detection methods, graph network generation) are not required, the proposed algorithm works very fast compared to the existing methods. The tree classification results are found reliable even on point clouds of cities containing many different objects. As the most significant weakness, false detection of light poles, traffic signs and other objects close to trees cannot be prevented. Nevertheless, the experimental results on mobile and airborne laser scanning point clouds indicate the possible usage of the algorithm as an important step for tree growth observation, tree counting and similar applications. While the laser scanning point cloud is giving opportunity to classify even very small trees, accuracy of the results is reduced in the low point density areas further away than the scanning location. These advantages and disadvantages of two laser scanning point cloud sources are discussed in detail.

scholarly journals Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 111 ◽  
Author(s):  
Anping Song ◽  
Zuoyu Wu ◽  
Xuehai Ding ◽  
Qian Hu ◽  
Xinyi Di
Keyword(s):  
Facial Nerve ◽  
Ground Truth ◽  
Detection Methods ◽  
Facial Nerve Paralysis ◽  
Physical Pain ◽  
Clinical Images ◽  
Nerve Paralysis ◽  
Standard Classification ◽  
Visual Diagnosis

Facial nerve paralysis (FNP) is the most common form of facial nerve damage, which leads to significant physical pain and abnormal function in patients. Traditional FNP detection methods are based on visual diagnosis, which relies solely on the physician’s assessment. The use of objective measurements can reduce the frequency of errors which are caused by subjective methods. Hence, a fast, accurate, and objective computer method for FNP classification is proposed that uses a single Convolutional neural network (CNN), trained end-to-end directly from images, with only pixels and disease labels as inputs. We trained the CNN using a dataset of 1049 clinical images and divided the dataset into 7 categories based on classification standards with the help of neurologists. We tested its performance against the neurologists’ ground truth, and our results matched the neurologists’ level with 97.5% accuracy.

Research of network attacks detection methods

2019 ◽  
pp. 55-59
Author(s):  
P. V. Frolov ◽  
E. V. Vershinin ◽  
S. A. Medvedeva
Keyword(s):  
Comparative Analysis ◽  
Intrusion Detection ◽  
Real Time ◽  
Suitable Method ◽  
Detection Methods ◽  
Network Attacks ◽  
Advantages And Disadvantages ◽  
Initial Analysis ◽  
F Measure

This paper reviews existing methods of network attacks detecting. A brief description of methods, their main features, advantages and disadvantages are given in accordance with the generally accepted classification of detection methods. During the initial analysis evidently inappropriate methods for this study were pointed out. Criteria for estimation of suitable methods for detecting cyberattacks in real time are given (recal, precision, F-measure). Each suitable method was estimated in accordance with the criteria. The comparative analysis of intrusion detection methods was carried out based on the obtained estimates. The most effective methods for solving problems of detecting cyberattacks in real time were chosen. A brief description of further research is given, which is based on the obtained results.

Research of methods of protecting organizations from inimical mergers and acquisitions

2020 ◽  
pp. 29-45
Author(s):  
O.A. Naydis ◽  
I.O. Naydis
Keyword(s):  
Comparative Analysis ◽  
Mergers And Acquisitions ◽  
Positive Effects ◽  
Advantages And Disadvantages ◽  
Preventive Methods

The article considers the types, forms, mechanisms and classification of mergers and acquisitions, identifies their positive effects, and studies the tactics of acquisitions. The analysis of anti-capture measures: active and preventive methods of protection against hostile mergers and acquisitions. A comparative analysis of anti-capture measures with acquisitions tactics was carried out, the advantages and disadvantages of their application were identified.

Classification of the factors forming the cost of the web-site

2017 ◽  
Vol 31 (2) ◽  
pp. 82-89
Author(s):  
E. S. Epifanov
Keyword(s):  
Web Site ◽  
Internet Site ◽  
Advantages And Disadvantages ◽  
Major Factors ◽  
The Cost ◽  
The Web

This article presents a classification of major factors that shape the cost of Internet site. Also discusses the limitations in determining the objectives of the web site; advantages and disadvantages of different factors.

Classification of ground-truth fire debris samples using artificial neural networks

2021 ◽  
Vol 23 ◽  
pp. 100313
Author(s):  
Nicholas A. Thurn ◽  
Taylor Wood ◽  
Mary R. Williams ◽  
Michael E. Sigman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ground Truth ◽  
Fire Debris ◽  
Artificial Neural

scholarly journals How Can I Grab That?

i-com ◽  
2020 ◽  
Vol 19 (2) ◽  
pp. 67-85
Author(s):  
Matthias Weise ◽  
Raphael Zender ◽  
Ulrike Lucke
Keyword(s):  
Virtual Reality ◽  
User Experience ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Advantages And Disadvantages ◽  
Multiple Dimensions ◽  
Application Developers ◽  
Three Dimensional Space

AbstractThe selection and manipulation of objects in Virtual Reality face application developers with a substantial challenge as they need to ensure a seamless interaction in three-dimensional space. Assessing the advantages and disadvantages of selection and manipulation techniques in specific scenarios and regarding usability and user experience is a mandatory task to find suitable forms of interaction. In this article, we take a look at the most common issues arising in the interaction with objects in VR. We present a taxonomy allowing the classification of techniques regarding multiple dimensions. The issues are then associated with these dimensions. Furthermore, we analyze the results of a study comparing multiple selection techniques and present a tool allowing developers of VR applications to search for appropriate selection and manipulation techniques and to get scenario dependent suggestions based on the data of the executed study.

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

scholarly journals Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiang Li ◽  
Jianzheng Liu ◽  
Jessica Baron ◽  
Khoa Luu ◽  
Eric Patterson
Keyword(s):  
Focal Length ◽  
Ground Truth ◽  
Detection Methods ◽  
Viewing Angle ◽  
Deep Convolutional Neural Networks ◽  
Landmark Detection ◽  
High Performing ◽  
Detection Techniques ◽  
And Performance ◽  
Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

Sign in / Sign up

Export Citation Format

Share Document