A Flexible Analysis Tool for the Quantitative Acoustic Assessment of Infant Cry

Reggiannini, Brian; Sheinkopf, Stephen J.; Silverman, H.F.; Li, Xiaoxue; Lester, Barry M.

doi:10.1044/1092-4388(2013/11-0298)

Cited by 26 publications

(25 citation statements)

References 32 publications

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“…Cry segmentation Simple Inverse Filter Tracking Orlandi et al 2012b Short Time Energy Dìaz et al 2012Orlandi et al 2012aManfredi et al 2018Várallyay 2006 Zero Crossing Rate Várallyay 2006Várallyay , 2007 Fourier Transform Várallyay 2007 Word reliability Yamamoto et al 2013 Classification K-Nearest Neighbor Reggiannini et al 2013 Hidden Markov Model Naithani et al 2018 Abou-Abbas et al 2015, 2017b Gaussian Mixture Model Abou-Abbas et al 2017b Logistic regression Lavner et al 2016Ferreti et al 2018 Convolutional Neural Networks Lavner et al2016Torres et al 2017Ferreti et al 2018 Cry classification Statistical analysis Grunau et al 1990Fuller 1991, Fuller and Conner 1995Johnston et al 1993Stevens et al 1994 Goberman Orlandi et al 2015O...…”

Section: Methods For Acoustic Signal Processing In Paediatricsmentioning

confidence: 99%

“…However, we reported two types of acoustic environments: i) with low energy noise (e.g. background noise) (Várallyay, 2006(Várallyay, , 2007Orlandi et al, 2012a;Díaz et al, 2012;Orlandi et al, 2012b;Reggiannini et al, 2013;Orlandi et al, 2013Orlandi et al, , 2015Orlandi et al, , 2016Manfredi et al, 2018) and ii) with high energy noise (e.g. medical device sounds, adults' voices) (Yamamoto et al, 2013;Abou-Abbas et al, 2015;Lavner et al, 2016;Abou-Abbas et al, 2017b;Naithani et al, 2018).…”

mentioning

confidence: 98%

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Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

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Objective: Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU). Approach: For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts—full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization. Main results: The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed. Significance: Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.

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Section: Methods For Acoustic Signal Processing In Paediatricsmentioning

confidence: 99%

mentioning

confidence: 98%

Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

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“…In the last twenty years, most of the research was devoted to F 0 estimation with traditional approaches such as FFT, correlation and cepstrum [13,[17][18][19][20]. Instead, few papers addressed the RFs estimation: in some papers F1 was estimated with FFT [10,18,21,22] and recently FFT is applied for F 1 -F 3 estimation [20]. In Robb et al [22], FFT and Linear Prediction (LP) methods were compared, with results comparable only for F1.…”

Section: Introductionmentioning

confidence: 99%

“…To the authors' knowledge only two software tools exist specifically designed for the automatic analysis of infant crying. The above mentioned BioVoice [7,23,[25][26][27], based on an innovative adaptive parametric approach for F 0 and formants estimation [10] and successfully tested against other software tools [11,28] and a software tool recently proposed by Reggiannini et al [20] that estimates F 0 by means of a cepstrum approach. [11].…”

Section: Introductionmentioning

confidence: 99%

Testing software tools for newborn cry analysis using synthetic signals

Orlandi

Bandini

Fiaschi

et al. 2017

Biomedical Signal Processing and Control

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a b s t r a c tContactless techniques are of increasing clinical interest as they can provide advantages in terms of comfort and safety of the patient with respect to sensor-based methods. Therefore, they are particularly well suited for vulnerable patients such as newborns. Specifically the acoustical analysis of the infant cry is a contactless approach to assist the clinical specialist in the detection of abnormalities in infants with possible neurological disorders. Along with the perceptual analysis, the automated analysis of infant cry is usually performed through software tools that however might not be devoted to this specific signal. The newborn cry is a signal extremely difficult to analyze with standard techniques due to its quasi-stationarity and to very high range of frequencies of interest. Therefore software tools should be specifically set and used with caution. To address this issue three methods are tested and compared, one freely available and other two specifically built using different approaches: autoregressive adaptive models and wavelets. The three methods are compared using synthetic signals coming from a synthesizer developed for the generation of basic melodic shapes of the newborn cry. Results point out strengths and weaknesses of each method, thus suggesting their most appropriate use according to the goals of the analysis.

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“…Several studies concern both the subjective auditory analysis of voice and speech and the automatic acoustical analysis in adults. However, with respect to the newborn cry, few automated methods exist, some of them based on classical approaches such as Fourier transform and autocorrelation analysis [2][3][4][5][6] and other on parametric techniques. [7][8][9] Such methods allow estimating the main acoustical features such as the frequency of vibration of the vocal folds, the vocal tract resonance frequencies, the cry duration, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry

et al. 2016

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Summary: Objectives. Scientific and clinical advances in perinatology and neonatology have enhanced the chances of survival of preterm and very low weight neonates. Infant cry analysis is a suitable noninvasive complementary tool to assess the neurologic state of infants particularly important in the case of preterm neonates. This article aims at exploiting differences between full-term and preterm infant cry with robust automatic acoustical analysis and data mining techniques. Study design. Twenty-two acoustical parameters are estimated in more than 3000 cry units from cry recordings of 28 full-term and 10 preterm newborns. Methods. Feature extraction is performed through the BioVoice dedicated software tool, developed at the Biomedical Engineering Lab, University of Firenze, Italy. Classification and pattern recognition is based on genetic algorithms for the selection of the best attributes. Training is performed comparing four classifiers: Logistic Curve, Multilayer Perceptron, Support Vector Machine, and Random Forest and three different testing options: full training set, 10-fold crossvalidation, and 66% split. Results. Results show that the best feature set is made up by 10 parameters capable to assess differences between preterm and full-term newborns with about 87% of accuracy. Best results are obtained with the Random Forest method (receiver operating characteristic area, 0.94). Conclusions. These 10 cry features might convey important additional information to assist the clinical specialist in the diagnosis and follow-up of possible delays or disorders in the neurologic development due to premature birth in this extremely vulnerable population of patients. The proposed approach is a first step toward an automatic infant cry recognition system for fast and proper identification of risk in preterm babies.

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A Flexible Analysis Tool for the Quantitative Acoustic Assessment of Infant Cry

Cited by 26 publications

References 32 publications

Video and audio processing in paediatrics: a review

Video and audio processing in paediatrics: a review

Testing software tools for newborn cry analysis using synthetic signals

Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry

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