The Study of Baby Crying Analysis Using MFCC and LFCC in Different Classification Methods

Dewi, Sita Purnama; Prasasti, Anggunmeka Luhur; Irawan, Budhi

doi:10.1109/icsigsys.2019.8811070

Cited by 42 publications

(15 citation statements)

References 9 publications

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“…The Mel scale is commonly an immediate preparation under 1,000 Hz and logarithmically isolated over 1,000 Hz. MFCC involves six computational advancements ( 3 ). This progression likewise includes passing the sign through the channel, which highlights higher recurrence in the frequency band.…”

Section: Data Preparationmentioning

confidence: 99%

A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification

Joshi

Srinivasan

Vincent

et al. 2022

Front. Public Health

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Understanding the reason for an infant's cry is the most difficult thing for parents. There might be various reasons behind the baby's cry. It may be due to hunger, pain, sleep, or diaper-related problems. The key concept behind identifying the reason behind the infant's cry is mainly based on the varying patterns of the crying audio. The audio file comprises many features, which are highly important in classifying the results. It is important to convert the audio signals into the required spectrograms. In this article, we are trying to find efficient solutions to the problem of predicting the reason behind an infant's cry. In this article, we have used the Mel-frequency cepstral coefficients algorithm to generate the spectrograms and analyzed the varying feature vectors. We then came up with two approaches to obtain the experimental results. In the first approach, we used the Convolution Neural network (CNN) variants like VGG16 and YOLOv4 to classify the infant cry signals. In the second approach, a multistage heterogeneous stacking ensemble model was used for infant cry classification. Its major advantage was the inclusion of various advanced boosting algorithms at various levels. The proposed multistage heterogeneous stacking ensemble model had the edge over the other neural network models, especially in terms of overall performance and computing power. Finally, after many comparisons, the proposed model revealed the virtuoso performance and a mean classification accuracy of up to 93.7%.

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Section: Data Preparationmentioning

confidence: 99%

A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification

Joshi

Srinivasan

Vincent

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…It is a cepstral representation of the audio signals. Researchers use it to test proposed approaches [17,29,49,52,57,[60][61][62] and often use it for baseline experiments [13,15,22,31,37,63]. Liu et al used MFCC along with two other cepstral features Linear Prediction Cepstral Coefficients (LPCC) and Bark Frequency Cepstral Coefficients (BFCC) for infant cry reason classification.…”

Section: Cepstral Domain Featuresmentioning

confidence: 99%

“…Linear Frequency Cepstral Coefficients (LFCC) extraction process is similar to MFCC extraction. The difference is that it uses a linear filter-bank instead of the Mel filter-bank [37,64]. In [22] and [65], the authors showed that LFCC performs better than MFCC in discriminating high frequency audio signals such as female voice and baby cry signals.…”

Section: Cepstral 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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“…Priscilla Dunstan found that every baby makes certain sounds while crying to convey their needs, such as Owh Heh, Eh, Eair, and Neh, representing tired, discomfort, burp or sleepy, pain, and hunger. Dewi et al ( 11 ) analyze the feature extraction techniques such as linear frequency cepstral coefficient and Mel frequency cepstral coefficient. It extracts the features from the spectrogram.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Ashwini

Vincent

Srinivasan

et al. 2021

Front. Public Health

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Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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The Study of Baby Crying Analysis Using MFCC and LFCC in Different Classification Methods

Cited by 42 publications

References 9 publications

A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification

A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification

A review of infant cry analysis and classification

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

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