Effective pre-processing of long term noisy audio recordings: An aid to clinical monitoring

A Narrative Review on Different Novel Machine Learning Techniques for Detecting Pathologies in Infants From Born Baby Cries

This paper reviews the research work on the analysis and classification of pathological infant cries in the last 50 years. The literature review mainly covers the need and role of early clinical diagnosis, pathologies detected from cry samples, challenges in pathological cry signal data acquisition, signal processing techniques, and signal classifiers. The signal processing techniques include preprocessing, feature extraction from domains, such as time, spectral, time-frequency, prosodic, wavelet, etc, and feature selection for selecting dominant features. Literature covers traditional machine learning classifiers, such as Bayesian networks, decision trees, K-nearest neighbor, support vector machine, Gaussian mixture model, etc, and recently added neural network models, such as convolutional neural networks, regression neural networks, probabilistic neural networks, graph neural networks, etc. Significant experimental results of pathological cry identification and classification are listed for comparison. Finally, it suggests future research in the direction of database preparation, feature analysis and extraction, neural network classifiers to provide a non-invasive and robust automatic infant cry analysis model.

Video and audio processing in paediatrics: a review

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.

Voice dosimetry and monitoring, with emphasis on professional voice diseases: Critical review and framework for future research

Professional voice has become an important issue in the field of occupational health. Similarly, voice diseases related to occupations gain interest in insurance medicine, particularly within the frame of specific insurance systems for occupational diseases. Technological developments have made possible dosimetry of voice loading in the work-place, as well as long-term monitoring of relevant voice parameters during professional activities. A critical review is given, with focus on the specificity of occupational voice use and on the point of view of insurance medicine. Remaining questions and suggestions for future research are proposed.