AICDS: An Infant Crying Detection System Based on Lightweight Convolutional Neural Network

Zhang, Xiaohu; Zou, Yuexian; Liu, Yi

doi:10.1007/978-3-319-94361-9_14

“…In [29], a specially-designed CNN was shown to outperform a traditional logistic regression-based classifier in very low false-positive rate regimes. In [30], a CNN running on audio captured from a microphone array installed next to a baby carriage could detect cry with 86% accuracy. [31] compared a CNN followed by a Hidden Markov Model (HMM) to a Linear Discriminant Analysis (LDA) classifier.…”

Section: Approaches In Audio Event Detectionmentioning

confidence: 99%

Baby Cry Detection: Deep Learning and Classical Approaches

Cohen

¹

,

Ruinskiy

²

,

Zickfeld

³

et al. 2019

Studies in Computational Intelligence

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In this chapter, we compare deep learning and classical approaches for detection of baby cry sounds in various domestic environments under challenging signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare their performance to that of classical machine-learning approaches, such as logistic regression and support vector machines. In addition to feedforward CNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized nonsymmetric kernels, better results are obtained compared to common CNN architectures.

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“…Table 1 shows the commonly used databases in recent research. Some databases are recorded in the Neonatal Intensive Care Unit (NICU), pediatric clinics, or baby-sitting environments [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Some cry audio signals online are also collected in [25].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Data augmentation techniques are used to artificially increase the data size. Zhang et al created new waveform images from training datasets by transforming these waveform images into slightly faster or slightly slower waveforms for the purpose of increasing training datasets to overcome overfitting problem [12]. In [43], several data augmentation techniques, such as noise variation, signal intensity variation, tonality variation, and spectrogram's size alteration, were used to artificially increase either the number of audio signals or the number of spectrograms.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In [24], Singh et al explored the residual MFCC and implicit LP residual features that represent excitation source information. Researchers have also tried other cepstral features such as Fast Fourier Transform (FFT) [23,66], Log-Mel feature [11,18], Mel Scale [43], Constant-Q Chromagram [43], Log-mel spectrum [12], and delta spectrum [12]. According to auditory perception models, MFCC coefficients are more robust than other coefficients such as LPC coefficients.…”

Section: Cepstral Domain Featuresmentioning

confidence: 99%

See 1 more Smart Citation

A review of infant cry analysis and classification

Ji

¹

,

Mudiyanselage

²

,

Gao

³

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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This paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

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“…In [29], a specially-designed CNN was shown to outperform a traditional logistic regression-based classifier in very low false-positive rate regimes. In [30], a CNN running on audio captured from a microphone array installed next to a baby carriage could detect cry with 86% accuracy. [31] compared a CNN followed by a Hidden Markov Model (HMM) to a Linear Discriminant Analysis (LDA) classifier.…”

Section: Approaches In Audio Event Detectionmentioning

confidence: 99%

Development and Analysis of Deep Learning Architectures

2020

Studies in Computational Intelligence

3

0

View full text Add to dashboard Cite

In this chapter, we compare deep learning and classical approaches for detection of baby cry sounds in various domestic environments under challenging signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare their performance to that of classical machine-learning approaches, such as logistic regression and support vector machines. In addition to feedforward CNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized nonsymmetric kernels, better results are obtained compared to common CNN architectures.

show abstract

AICDS: An Infant Crying Detection System Based on Lightweight Convolutional Neural Network

Cited by 6 publications

References 13 publications

Baby Cry Detection: Deep Learning and Classical Approaches

Baby Cry Detection: Deep Learning and Classical Approaches

A review of infant cry analysis and classification

Development and Analysis of Deep Learning Architectures

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