Effective pre-processing of long term noisy audio recordings: An aid to clinical monitoring
(2013) Biomedical Signal Processing and Control — Vol. 8, n° 6, p. 799-810 (2013)
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- Nowadays, great attention is devoted to minimizing the discomfort caused by connection of patients to sensors for long-term monitoring of physiological parameters. Hence, the need for contact-less monitoring systems is increasingly recognized in clinical investigation. To this aim, audio signals recorded by ambient microphones are an appealing and increasing field of research: in the biomedical field, application of contact-less audio recording of long duration may concern obstructive apnoea syndrome, preterm newborns in Intensive Care Units, daily monitoring in occupational dysphonia, speech therapy, Parkinson and Alzheimer disease, monitoring of psychiatric and autistic subjects, etc. However, a significant amount of ambient noise is inevitably included in the records. Especially in the case of recordings that take a long time, manual extraction of clinically useful information from a whole record is a time-consuming operator-dependent task, the length of a whole recording (even several hours) being prohibitive both for perceptual analysis made by listening to it and for visual inspection of signal patterns. Moreover, objective measures of signal characteristics may serve clinicians as a common ground for diagnosis. Hence, automatic methods are needed to speed up and objectify the analysis task. The present work describes a new, automatic, fast and reliable method for extracting "voiced candidates" from audio recordings of long duration for both clinical and home applications. To demonstrate its effectiveness, the method is compared to existing software tools commonly used in biomedical applications using synthetic signals. © 2013 Elsevier Ltd.
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Orlandi, S., Dejonckere, P. H., Schoentgen, J., Lebacq, J., Rruqja, N., & Manfredi, C. (2013). Effective pre-processing of long term noisy audio recordings: An aid to clinical monitoring. Biomedical Signal Processing and Control, 8(6), 799-810. https://doi.org/10.1016/j.bspc.2013.07.009 (Original work published 2013)