Anshu Chittora

Classification of normal and pathological infant cries using bispectrum features

In this paper, bispectrum-based feature extraction method is proposed for classification of normal vs. pathological infant cries. Bispectrum is computed for all segments of normal as well as pathological cries. Bispectrum is a two-dimensional (2-D) feature. A tensor is formed using these bispectrum features and then for feature reduction, higher order singular value decomposition theorem (HOSVD) is applied. Our experimental results show 70.56 % average accuracy of classification with support vector machine (SVM) classifier, whereas baseline features, viz., MFCC, LPC and PLP gave classification accuracy of 52.41 %, 61.27 % and 57.41 %, respectively. For showing the effectiveness of the proposed feature extraction method, a comparison with other feature extraction methods which uses diagonal slice and peaks and their locations as feature vectors is given as well.

Classification of pathological infant cries using modulation spectrogram features

In this paper, feature derived from modulation spectrogram is proposed for the classification of pathological infant cries. In our work, two pathologies are considered, viz., asthma and hypoxy ischemic encephalopathy (HIE). Modulation spectrogram features are arranged in a form of tensor which is then reduced in its dimensions using Higher Order Singular Value Decomposition Theorem (HOSVD). The reduced feature set is used for classification of pathological infant cries using support vector machine (SVM) classifier with radial basis function (RBF) kernel. The classifier gives a mean classification accuracy of 76.23 % with the proposed feature set. The same experimental setup is used for the conventional feature set, i.e., Mel frequency cepstral coefficients (MFCC). MFCC shows a classification accuracy of 64.43 %. It is also observed that the proposed approach is robust against signal degradation conditions.

Analysis of normal and pathological infant cries using bispectrum features derived using HOSVD

In this paper, bispectrum-based feature extraction method is proposed for classification of normal vs. pathological infant cries. Bispectrum is a class of higher order spectral analysis, Bispectrum is computed for all segments of normal as well as pathological cries. Bispectrum is a two-dimensional (i.e., 2-D) feature. A tensor is formed using these bispectrum features and then for feature reduction, higher order singular value decomposition theorem (HOSVD) is applied. Our experimental results show 98.94 % average accuracy of classification with support vector machine (SVM) classifier whereas baseline features, viz., Mel frequency cepstral coefficients (MFCC), perceptual linear prediction coefficients (PLP) and linear prediction coefficients (LPC) gave classification accuracy of 53.99 %, 63.14 % and 63.07 %, respectively. High classification accuracy of bispectrum can be attributed to its ability to capture nonlinearity in the signal.

Data Collection of Infant Cries for Research and Analysis.

Analysis of infants cries may help in identifying the needs of infants such as hunger, pain, sickness, etc and thereby develop a tool or possible mobile application that can help the parents in monitoring the needs of their infant. Analysis of cries of infants who are suffering from neurologic disorders and severe diseases, which can later on result in motor and mental handicap, may prove helpful in early diagnosis of pathologies and protect infants from such disorders. The development of an infant cry corpus is necessary for the analysis of infant cries and for the development of infant cry tools. Infant cry database is not available commercially for research, which limits the scope of research in this area. Because the cry characteristics changes with many factors such as reason for crying, infants health and weight, age, etc, care is required while designing a corpus for a particular research application of infant cry analysis and classification. In this paper, the ideal characteristics of the corpus are proposed along with factors influencing infant cry characteristics, and experiences during data collection are shared. This study may help other researchers to build an infant cry corpus for their specific problem of study. Justification of the proposed characteristics is also given along with suitable examples.

Modified Group Delay Based Features for Asthma and HIE Infant Cries Classification

Modified group delay features have shown promising results for automatic speech recognition ASR task. In this paper, features are derived from the modified group delay function. These features are then used to classify asthma and hypoxy ischemic encephalopathy HIE infant cries. Our experimental results show that the performance of the proposed features is better than the state-of-the-art feature set, i.e., Mel frequency cepstral coefficients MFCC. Best classification accuracy is achieved with the proposed features is 90.38 % as opposed to 84.92 % obtained with MFCC, when applied to a SVM classifier with radial basis function kernel. The proposed feature set performs much better for classification of asthma infant cries. However, for HIE both features perform equally well. The class separability distance of the group delay feature is higher than the MFCC feature for most of the features Bhattacharya class separation distance is higher by 0.2 units compared to MFCC, which also confirms that the proposed features are better than MFCC.

Significance of Unvoiced Segments and Fundamental Frequency in Infant Cry Analysis

In this paper, significance of unvoiced segments and fundamental frequency

Classification of phonemes using modulation spectrogram based features for Gujarati language

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Classification of Fricatives Using Novel Modulation Spectrogram Based Features

in infant cry analysis is investigated. To find out the unvoiced segments from the infant cry