scholarly journals Disrupting Audio Event Detection Deep Neural Networks with White Noise

Technologies ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 64
Author(s):  
Rodrigo dos Santos ◽  
Ashwitha Kassetty ◽  
Shirin Nilizadeh
Keyword(s):  
Neural Networks ◽  
White Noise ◽  
Convolutional Neural Networks ◽  
Event Detection ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Audio Event ◽  
Noise Disturbances ◽  
Classification Tasks ◽  
Percent Success

Audio event detection (AED) systems can leverage the power of specialized algorithms for detecting the presence of a specific sound of interest within audio captured from the environment. More recent approaches rely on deep learning algorithms, such as convolutional neural networks and convolutional recurrent neural networks. Given these conditions, it is important to assess how vulnerable these systems can be to attacks. As such, we develop AED-suited convolutional neural networks and convolutional recurrent neural networks, and attack them next with white noise disturbances, conceived to be simple and straightforward to be implemented and employed, even by non-tech savvy attackers. We develop this work under a safety-oriented scenario (AED systems for safety-related sounds, such as gunshots), and we show that an attacker can use such disturbances to avoid detection by up to 100 percent success. Prior work has shown that attackers can mislead image classification tasks; however, this work focuses on attacks against AED systems by tampering with their audio rather than image components. This work brings awareness to the designers and manufacturers of AED systems, as these solutions are vulnerable, yet may be trusted by individuals and families.

scholarly journals Inter-Class Angular Loss for Convolutional Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 3894-3901 ◽  
Author(s):  
Le Hui ◽  
Xiang Li ◽  
Chen Gong ◽  
Meng Fang ◽  
Joey Tianyi Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Learning Difficulties ◽  
Feature Space ◽  
Superior Performance ◽  
Strongly Correlated ◽  
Discriminative Ability ◽  
Practical Applications ◽  
Classification Tasks

Convolutional Neural Networks (CNNs) have shown great power in various classification tasks and have achieved remarkable results in practical applications. However, the distinct learning difficulties in discriminating different pairs of classes are largely ignored by the existing networks. For instance, in CIFAR-10 dataset, distinguishing cats from dogs is usually harder than distinguishing horses from ships. By carefully studying the behavior of CNN models in the training process, we observe that the confusion level of two classes is strongly correlated with their angular separability in the feature space. That is, the larger the inter-class angle is, the lower the confusion will be. Based on this observation, we propose a novel loss function dubbed “Inter-Class Angular Loss” (ICAL), which explicitly models the class correlation and can be directly applied to many existing deep networks. By minimizing the proposed ICAL, the networks can effectively discriminate the examples in similar classes by enlarging the angle between their corresponding class vectors. Thorough experimental results on a series of vision and nonvision datasets confirm that ICAL critically improves the discriminative ability of various representative deep neural networks and generates superior performance to the original networks with conventional softmax loss.

Device Invariant Deep Neural Networks for Pulmonary Audio Event Detection Across Mobile and Wearable Devices

2021 ◽  
Author(s):  
Mohsin Y Ahmed ◽  
Li Zhu ◽  
Md Mahbubur Rahman ◽  
Tousif Ahmed ◽  
Jilong Kuang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Event Detection ◽  
Deep Neural Networks ◽  
Wearable Devices ◽  
Audio Event

scholarly journals Learning Sound Events from Webly Labeled Data

2019 ◽  
Author(s):  
Anurag Kumar ◽  
Ankit Shah ◽  
Alexander Hauptmann ◽  
Bhiksha Raj
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Learning Process ◽  
Event Detection ◽  
Deep Neural Networks ◽  
Baseline Method ◽  
Audio Recordings ◽  
Audio Data ◽  
Audio Event ◽  
The Web

In the last couple of years, weakly labeled learning has turned out to be an exciting approach for audio event detection. In this work, we introduce webly labeled learning for sound events which aims to remove human supervision altogether from the learning process. We first develop a method of obtaining labeled audio data from the web (albeit noisy), in which no manual labeling is involved. We then describe methods to efficiently learn from these webly labeled audio recordings. In our proposed system, WeblyNet, two deep neural networks co-teach each other to robustly learn from webly labeled data, leading to around 17% relative improvement over the baseline method. The method also involves transfer learning to obtain efficient representations.

scholarly journals Audio Event Detection Using Deep Neural Networks

2017 ◽  
Vol 18 (1) ◽  
pp. 183-190 ◽  
Author(s):  
Minkyu Lim ◽  
Donghyun Lee ◽  
Hosung Park ◽  
Ji-Hwan Kim
Keyword(s):  
Neural Networks ◽  
Event Detection ◽  
Deep Neural Networks ◽  
Audio Event

scholarly journals Real-time classification of hand movements as a basis for intuitive control of grasp neuroprostheses

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dmitry Amelin ◽  
Ivan Potapov ◽  
Josep Cardona Audí ◽  
Andreas Kogut ◽  
Rüdiger Rupp ◽  
...  
Keyword(s):  
Neural Networks ◽  
Standard Deviation ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Healthy Subjects ◽  
Hand Movements ◽  
Cord Injury ◽  
Field Programmable

AbstractThis paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury. A field-programmable gate array has been chosen as an implementation platform due to its form factor and ability to perform parallel computations, which are specific for the selected neural networks. Three different phases of two grasp patterns and the additional open hand pattern were predicted by means of surface Electromyography (EMG) signals (i.e. Seven classes in total). Across seven healthy subjects, CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) had a mean accuracy of 85.23% with a standard deviation of 4.77% and 112 µs per prediction and 83.30% with a standard deviation of 4.36% and 40 µs per prediction, respectively.

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