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 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 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 Audio Event Classification Using Deep Neural Networks

2015 ◽  
Vol 7 (4) ◽  
pp. 27-33
Author(s):  
Minkyu Lim ◽  
Donghyun Lee ◽  
Kwang-Ho Kim ◽  
Ji-Hwan Kim
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Event Classification ◽  
Audio Event

scholarly journals Automatic real-time gait event detection in children using deep neural networks

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0211466 ◽  
Author(s):  
Łukasz Kidziński ◽  
Scott Delp ◽  
Michael Schwartz
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Event Detection ◽  
Deep Neural Networks
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