# A Real-Time Audio Monitoring Framework with Limited Data for Constrained Devices

@article{Salekin2019ARA,
title={A Real-Time Audio Monitoring Framework with Limited Data for Constrained Devices},
author={Asif Salekin and Shabnam Ghaffarzadegan and Z. Feng and John A. Stankovic},
journal={2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)},
year={2019},
pages={98-105}
}
• Published 1 May 2019
• Computer Science
• 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)
An effective and non-invasive audio monitoring system needs to be capable of simultaneous real-time detection of multiple audio events in many different environments, and locally executable on resource constrained devices, such as, smart microphones. A major challenge in this research domain is having limited available annotated data. This paper presents a novel framework to generate robust detection models of environmental and human audio events with limited available data. The framework…

## References

SHOWING 1-10 OF 28 REFERENCES
Reliable detection of audio events in highly noisy environments
• Computer Science
Pattern Recognit. Lett.
• 2015
Audio Based Event Detection for Multimedia Surveillance
• Computer Science
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings
• 2006
The results show that the proposed top-down event detection approach works significantly better than the single level approach.
Events Detection for an Audio-Based Surveillance System
• Computer Science
2005 IEEE International Conference on Multimedia and Expo
• 2005
The automatic shot detection system presented is based on a novelty detection approach which offers a solution to detect abnormality (abnormal audio events) in continuous audio recordings of public places and takes advantage of potential similarity between the acoustic signatures of the different types of weapons by building a hierarchical classification system.
Robust audio-codebooks for large-scale event detection in consumer videos
• Computer Science
INTERSPEECH
• 2013
This work empirically evaluate several approaches to model expressive and robust audio codebooks for the task of MED while ensuring compactness and applies text based techniques like Latent Dirichlet Allocation to learn acoustictopics as a means of providing compact representation while maintaining performance.
Audio-based multimedia event detection using deep recurrent neural networks
• Computer Science
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 2016
This paper introduces longer-range temporal information with deep recurrent neural networks (RNNs) for both stages ofimedia event detection, and observes improvements in both frame-level and clip-level performance compared to SVM and feed-forward neural network baselines.
An Adaptive Framework for Acoustic Monitoring of Potential Hazards
• Computer Science
EURASIP J. Audio Speech Music. Process.
• 2009
This work presents an efficient methodology for acoustic surveillance of atypical situations which can find use under different acoustic backgrounds using a probabilistic hierarchical scheme designed based on Gaussian mixture models and state-of-the-art sound parameters selected through extensive experimentation.
Data-efficient weakly supervised learning for low-resource audio event detection using deep learning
• Computer Science
DCASE
• 2018
A data-efficient training of a stacked convolutional and recurrent neural network is proposed in a multi instance learning setting for which a new loss function is introduced that leads to improved training compared to the usual approaches for weakly supervised learning.
Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data
• Computer Science
ArXiv
• 2017
A robust and efficient deep convolutional neural network (CNN) based framework to learn audio event recognizers from weakly labeled data that can train from and analyze recordings of variable length in an efficient manner and outperforms a network trained with {\em strongly labeled} web data by a considerable margin.
Recurrent neural networks for polyphonic sound event detection in real life recordings
• Computer Science
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 2016
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs). A single
Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Recognition
• Computer Science
INTERSPEECH
• 2016
This work introduces a convolutional neural network (CNN) with a large input field for AED that significantly outperforms state of the art methods including Bag of Audio Words (BoAW) and classical CNNs, achieving a 16% absolute improvement.