Baby Cry Detection in Domestic Environment Using Deep Learning

2016 ICSEE International Conference on the Science of Electrical Engineering

5 Pages Posted: 10 Dec 2016

See all articles by Yizhar Lavner

Yizhar Lavner

Tel-Hai Academic College

Rami Cohen

Technion-Israel Institute of Technology

Dima Ruinskiy

Intel Corporation

Hans IJzerman

Université Grenoble Alpes

Date Written: November 29, 2016

Abstract

Automatic detection of a baby cry in audio signals is an essential step in applications such as remote baby monitoring. It is also important for researchers, who study the relation between baby cry patterns and various health or developmental parameters. In this paper, we propose two machine-learning algorithms for automatic detection of baby cry in audio recordings. The first algorithm is a low-complexity logistic regression classifier, used as a reference. To train this classifier, we extract features such as Mel-frequency cepstrum coefficients, pitch and formants from the recordings. The second algorithm uses a dedicated convolutional neural network (CNN), operating on log Mel-filter bank representation of the recordings. Performance evaluation of the algorithms is carried out using an annotated database containing recordings of babies (0-6 months old) in domestic environments. In addition to baby cry, these recordings contain various types of domestic sounds, such as parents talking and door opening. The CNN classifier is shown to yield considerably better results compared to the logistic regression classifier, demonstrating the power of deep learning when applied to audio processing.

Keywords: baby cry detection, attachment, deep learning, infant research

Suggested Citation

Lavner, Yizhar and Cohen, Rami and Ruinskiy, Dima and IJzerman, Hans, Baby Cry Detection in Domestic Environment Using Deep Learning (November 29, 2016). 2016 ICSEE International Conference on the Science of Electrical Engineering. Available at SSRN: https://ssrn.com/abstract=2877132 or http://dx.doi.org/10.2139/ssrn.2877132

Yizhar Lavner (Contact Author)

Tel-Hai Academic College ( email )

Upper Galilee
Galil Elyon, 12210
Israel

Rami Cohen

Technion-Israel Institute of Technology ( email )

Technion City
Haifa 32000, Haifa 32000
Israel

Dima Ruinskiy

Intel Corporation ( email )

United States

Hans IJzerman

Université Grenoble Alpes ( email )

Grenoble
France

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