Pre-processing techniques for improved detection of vocalization sounds in a neonatal intensive care unit
| Autor: | Climent Nadeu, Ana Riverola de Veciana, Sergio Vidiella Pinto, Blanca Muñoz Mahamud, Oriol Ros Fornells, Ganna Raboshchuk |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
| Rok vydání: | 2018 |
| Předmět: |
Neonatal intensive care unit
health care facilities manpower and services Speech recognition education Health Informatics Spectral subtraction Non-negative matrix factorization 03 medical and health sciences 0302 clinical medicine Discriminative model 030225 pediatrics Noise control otorhinolaryngologic diseases Medicine Noise reduction Audio signal 030504 nursing business.industry Enginyeria biomèdica [Àrees temàtiques de la UPC] Sound detection Vocalization detection Soroll -- Control Signal Processing Auditory stimuli Enginyeria biomèdica False alarm 0305 other medical science business Biomedical engineering |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| ISSN: | 1746-8094 |
| Popis: | The sounds occurring in the noisy acoustical environment of a Neonatal Intensive Care Unit (NICU) are thought to affect the growth and neurodevelopment of preterm infants. Automatic sound detection in a NICU is a novel and challenging problem, and it is an essential step in the investigation of how preterm infants react to auditory stimuli of the NICU environment. In this paper, we present our work on an automatic system for detection of vocalization sounds, which are extensively present in NICUs. The proposed system reduces the presence of irrelevant sounds prior to detection. Several pre-processing techniques are compared, which are based on either spectral subtraction or non-negative matrix factorization, or a combination of both. The vocalization sounds are detected from the enhanced audio signal using either generative or discriminative classification models. An audio database acquired in a real-world NICU environment is used to assess the performance of the detection system in terms of frame-level missing and false alarm rates. The inclusion of the enhancement pre-processing step leads to up to 17.54% relative improvement over the baseline. |
| Databáze: | OpenAIRE |
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