Infant Weeping Calls Decoder using Statistical Feature Extraction and Gaussian Mixture Models

Sharma, Kuldeep; Gupta, Chirag; Gupta, Sandeep

doi:10.1109/icccnt45670.2019.8944527

Cited by 19 publications

(6 citation statements)

References 17 publications

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“…While there have been no groups using the same methods combined with this idea, there have been groups with a similar idea and separate execution, with their own pros and cons. For example, most recently a group participating in the 2019 10th International Conference on Computing, Communication and Networking Technologies tackled this issue by using Statistical Feature Extraction and Gaussian Mixture Models [5]. This team achieved an accuracy of 81.27%.…”

Section: Existing Methodsmentioning

confidence: 99%

“…As mentioned above, the most recent idea used Statistical Feature Extraction and Gaussian Mixture Models and was completed with an accuracy of 81.27% identifying a total of five different reasons for crying [5]. Gaussian Mixture Models are probability density functions that represent the weighted sums of component densities of a Gaussian [6].…”

Section: Methods Usedmentioning

confidence: 99%

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Baby Cry Classifications using Deep Learning

Grayson¹,

Zhu²

2021

Advances in Machine Learning, Data Mining and Computing

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New parents are frequently awakened by the cries of their newborn babies. Attempts to stop these cries sometimes result in increasingly louder cries. By first transforming these cries into waveforms, and then into sound spectrograms, the efficiencies and accuracies of different computer learning modules were tested: a support vector machine, a 2-layer neural network, and a long short-term memory model. Finally, an automatic sorter that categorizes each cry was developed. Using this method, it is possible to eliminate error and time wastage when trying to calm a baby. The results of testing the programs demonstrate a high accuracy rate for determining the source of a baby’s cries. This program will enable parents to calm their crying babies in a shorter amount of time, giving them more peace of mind, and perhaps allowing them to get more sleep.

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Section: Existing Methodsmentioning

confidence: 99%

Section: Methods Usedmentioning

confidence: 99%

Baby Cry Classifications using Deep Learning

Grayson¹,

Zhu²

2021

Advances in Machine Learning, Data Mining and Computing

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“…This is understandable because babies at this age need to double their weight within months [18]. In respect of "Donate a cry corpus" [14], our CNN model had shown better result compared with clustering method using statistical feature extraction and gaussian mixture models [12] which is having an accuracy of 81.27%. Again with the same source, the other test with the machine learning method was implemented for Automated Baby Monitoring which could reach 96% precision.…”

Section: Machine Learning and Databasementioning

confidence: 97%

“…It could also use a webcam to monitor Baby activity [11]. In order to help the nurse, a crying decoder should be helpful enough to calm and nurse the baby [12]. An IoT system would be used here to provide the communication facility.…”

Section: Introductionmentioning

confidence: 99%

Cry Recognition for Infant Incubator Monitoring System Based on Internet of Things using Machine Learning

Sutanto¹,

Fahmi²,

Shalannanda³

et al. 2021

IJIES

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With the current technology trend of IoT and Smart Device, there is a possibility for the improvement of our infant incubator in responding to the real baby’s condition. This work is trying to see that possibility. First is by analyzing of open baby voice database. From there, a procedure to find out baby cry classification will be explained. The approach was starting with an analysis of sound’s power from that WAV files before going further into the 2D pattern, which will have features for the machine learning. From this work, around 85% accuracy could be achieved. Then together with sensors, it would be useful for infant incubator’s innovation by utilizing this proposed configuration.

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“…Researchers also calculate the statistical natural parameters of the data such as mean frequency, standard deviation, and third quartile range, etc. to help infant cry detection and classification [39]. Feature extraction is a critical step in audio processing.…”

Section: Other Relevant Domain Featuresmentioning

confidence: 99%

A review of infant cry analysis and classification

Mudiyanselage

Gao

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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Infant Weeping Calls Decoder using Statistical Feature Extraction and Gaussian Mixture Models

Cited by 19 publications

References 17 publications

Baby Cry Classifications using Deep Learning

Baby Cry Classifications using Deep Learning

Cry Recognition for Infant Incubator Monitoring System Based on Internet of Things using Machine Learning

A review of infant cry analysis and classification

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