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2017 | OriginalPaper | Chapter

Baby Cry Sound Detection: A Comparison of Hand Crafted Features and Deep Learning Approach

Authors : Rafael Torres, Daniele Battaglino, Ludovick Lepauloux

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

Baby cry sound detection allows parents to be automatically alerted when their baby is crying. Current solutions in home environment ask for a client-server architecture where an end-node device streams the audio to a centralized server in charge of the detection. Even providing the best performances, these solutions raise power consumption and privacy issues. For these reasons, interest has recently grown in the community for methods which can run locally on battery-powered devices. This work presents a new set of features tailored to baby cry sound recognition, called hand crafted baby cry (HCBC) features. The proposed method is compared with a baseline using mel-frequency cepstrum coefficients (MFCCs) and a state-of-the-art convolutional neural network (CNN) system. HCBC features result to be on par with CNN, while requiring less computation effort and memory space at the cost of being application specific.

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Footnotes
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Metadata
Title
Baby Cry Sound Detection: A Comparison of Hand Crafted Features and Deep Learning Approach
Authors
Rafael Torres
Daniele Battaglino
Ludovick Lepauloux
Copyright Year
2017
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-65171-2

Electronic ISBN: 978-3-319-65172-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65172-9_15

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