Typical vs. atypical: Combining auditory Gestalt perception and acoustic analysis of early vocalisations in Rett syndrome

Pokorny, Florian B.; Bartl-Pokorny, Katrin D.; Einspieler, Christa; Zhang, Dajie; Vollmann, Ralf; Bölte, Sven; Gugatschka, Markus; Schuller, Björn; Marschik, Peter B.

doi:10.1016/j.ridd.2018.02.019

Cited by 18 publications

(18 citation statements)

References 44 publications

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“…Later, they evaluated more than 6000 features to differentiate typical and atypical early speech language of one infant with Rett syndrome. Main differences were observed in auditory attributes such as timbre and pitch (Pokorny et al, 2018).…”

Section: Automatic Cry Segmentationmentioning

confidence: 97%

“…3.2.6. Other sound assessment Several recent audio processing methods have been proposed regarding non-cry signals and concerning either pre-linguistic vocalizations (including cooing) (Fuller andHorii, 1986, 1988;Pokorny et al, 2016Pokorny et al, , 2018. Non-voice analyses were also proposed in different contexts such as external noise detection (Raboshchuk et al, 2018a,b), EEG sonification (Gomez et al, 2018) or lung sound assessment (Emmanouilidou et al, 2017).…”

Section: Automatic Cry Segmentationmentioning

confidence: 99%

“…It was used in (Suaste-Rivas et al, 2004;Reyes-Galaviz et al, 2005;Galaviz and García, 2005;Barajas-Montiel and Reyes-García, 2006;Díaz et al, 2012;Shinya et al, 2014;Rosales-Pérez et al, 2015;Borysiak et al, 2016;Abou-Abbas et al, 2017a). Acoustic parameters have also been extracted by the mean of the openSMILE toolkit (Eyben et al, 2013;Pokorny et al, 2016Pokorny et al, , 2018. Both softwares provided automatic computation of a wide variety of features (e.g.…”

mentioning

confidence: 99%

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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: Automatic Cry Segmentationmentioning

confidence: 97%

Section: Automatic Cry Segmentationmentioning

confidence: 99%

mentioning

confidence: 99%

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Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

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“…Therefore, in future studies it would be interesting to examine regression both in a dimensional and a categorical way and compare the results (Ozonoff, Heung, et al, 2008;Thurm et al, 2014). Up till now, only one recent prospective longitudinal study what we see into a more signal based and machine learning approach of analyzing audio-video data (e.g., Marschik et al, 2017;Pokorny et al, 2017Pokorny et al, , 2018). These analyses on signal level can be applied in both future retrospective and prospective studies on onset patterns in ASD.…”

Section: Future Studies Combining Categorical and Dimensional Conceptmentioning

confidence: 99%

Regression in autism spectrum disorder: A critical overview of retrospective findings and recommendations for future research

Boterberg

Charman

Marschik

et al. 2019

Neuroscience & Biobehavioral Reviews

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“…Building on our experience in collecting and analyzing preverbal data of typical and atypical development (e.g., Bartl-Pokorny et al 2013;Marschik et al 2012aMarschik et al , b, 2013Marschik et al , 2014aMarschik et al , b, 2017Pokorny et al 2016aPokorny et al , 2017Pokorny et al , 2018, we provide a methodological overview of current strategies for the collection and representation of preverbal data for intelligent audio analysis purposes. Exemplified on the basis of empirical data, we will especially focus on application-oriented challenges and constraints that have to be considered when dealing with preverbal data of individuals with late detected developmental disorders.…”

Section: Introductionmentioning

confidence: 99%

Efficient Collection and Representation of Preverbal Data in Typical and Atypical Development

et al. 2020

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Human preverbal development refers to the period of steadily increasing vocal capacities until the emergence of a child's first meaningful words. Over the last decades, research has intensively focused on preverbal behavior in typical development. Preverbal vocal patterns have been phonetically classified and acoustically characterized. More recently, specific preverbal phenomena were discussed to play a role as early indicators of atypical development. Recent advancements in audio signal processing and machine learning have allowed for novel approaches in preverbal behavior analysis including automatic vocalization-based differentiation of typically and atypically developing individuals. In this paper, we give a methodological overview of current strategies for collecting and acoustically representing preverbal data for intelligent audio analysis paradigms. Efficiency in the context of data collection and data representation is discussed. Following current research trends, we set a special focus on challenges that arise when dealing with preverbal data of individuals with late detected developmental disorders, such as autism spectrum disorder or Rett syndrome.

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Typical vs. atypical: Combining auditory Gestalt perception and acoustic analysis of early vocalisations in Rett syndrome

Cited by 18 publications

References 44 publications

Video and audio processing in paediatrics: a review

Video and audio processing in paediatrics: a review

Regression in autism spectrum disorder: A critical overview of retrospective findings and recommendations for future research

Efficient Collection and Representation of Preverbal Data in Typical and Atypical Development

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