Dual tree complex Wavelet Packet Transform based infant cry classification

Lim, Wei Jer; Hariharan, M.; Yazid, Haniza; Yaacob, Sazali; Nadarajaw, Thiyagar

doi:10.1063/1.4965169

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…This study ( 9 ) investigated the feature extracted from wavelet packet transform based on complex dual-tree form to discriminate the three sets of infant cries such as normal vs. asphyxia, normal vs. deaf, and hunger vs. pain. Various feature selection techniques such as correlation feature selection, principal component analysis, and information gain were applied to select the most relevant and essential features.…”

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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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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“…In general, methods for scoring features can be divided into four classes: agreement-based, information theory, statistical-based, and sparse learning based [ 31 ]. So far, researchers have proposed several feature scoring methods, such as in [ 32 , 33 ]. In unsupervised feature selection, non-negative Laplacian is used to estimate the feature contribution [ 34 ].…”

Section: Literature Reviewmentioning

confidence: 99%

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Asghar

Khan

Rizwan

et al. 2020

Sensors

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Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.

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“…Motivated by the previous studies, this study was undertaken to improve the performance of recognition rate of speaker and accent using real and complex wavelet transform namely WPT, DWPT and DT-CWPT. The studies have shown that the usage of wavelet gives a good result [12][13][14], however huge features set produced by wavelets affecting the processing time which is known as "curse of dimensionality". Number of smaller features are always preferred for the success of speaker/speech recognition system due to faster learning and improved performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The DT-CWPT consists of two DWPT operating in parallel on an input signal. The second wavelet packet filter bank is obtained by replacing the first stage filter h (1) i (n) by h and directional selectivity provides an accurate measure of spectral energy at a particular location in space, scale and orientation [13]. In this paper, input speech signals were decomposed into 5 levels using DT-CWPT which produced 124 wavelet packet coefficients.…”

Section: Dual Tree Complex Wavelet Packet Transform (Dt-cwpt)mentioning

confidence: 99%

Real and Complex Wavelet Transform Using Singular Value Decomposition for Malaysian Speaker and Accent Recognition

Abdullah

Vijean

Hariharan

et al. 2020

Lecture Notes in Mechanical Engineering

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This paper presents a new approach for Malaysian speaker and accent recognition using wavelet feature extraction method, namely Wavelet Packet Transform (WPT), Discrete Wavelet Packet Transform (DWPT) and Dual Tree Complex Wavelet Packet Transform (DT-CWPT). Since Singular Value Decomposition (SVD) was based on factorization and summarization technique which reduces a rectangular matric, it is applied on those features to evaluate the performance for speaker and accent recognition. The features are derived from wavelets and SVD classified with three different classifiers namely k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). In this work, English digits (0-9) and Malay words database uttered from 75 undergraduate students of Universiti Malaysia Perlis (UniMAP) which are Malays, Chinese and Indian. The Malay words had a combination of consonants and vowels in monosyllable and bi-syllable structure. The accuracy of file-based analysis achieved were above 81% while for frame-based analysis, 93.87% and above were obtained using three different classifiers (k-NN, SVM and ELM) for speaker and accent recognition. Through the experiments, it is observed that accent recognition achieved high recognition rate of 100% for both framed-based analysis and file-based analysis using SVM. The experimental results show the proposed features using SVD achieved high accuracy of 100% using SVM through English digits and Malay words in accent recognition. This indicated that feature extraction using wavelets (WPT, DWPT and DT-CWPT) with SVD can achieve a good performance for both English digits and Malay words.

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Dual tree complex Wavelet Packet Transform based infant cry classification

Cited by 4 publications

References 10 publications

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Real and Complex Wavelet Transform Using Singular Value Decomposition for Malaysian Speaker and Accent Recognition

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