Saša Saftić (2017) System for audio capture and classification of baby cry samples. MSc thesis.
Abstract
We explore multiclass classification of infants' cries and the relation between the age of the infant and the accuracy of classification. Additionally we explore secure cloud storage and cloud data processing. We compare several state-of-the-art multiclass classification models with recurrent neural networks. Classification accuracy was obtained on data from infants of various ages. For data storage and processing we used the Django Rest API and the opensource cloud platform OpenStack. Multiclass classification models successfully differentiated between different classes of crying, but no age effect has been found. We have demonstrated the aptness of the Django Rest API and OpenStack platform for data storing and processing in the cloud.
| Item Type: | Thesis (MSc thesis) |
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| Keywords: | infant cry, data acquisition, audio analysis, feature extraction, classification, cloud processing, cloud security |
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| Number of Pages: | 80 |
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| Language of Content: | Slovenian |
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| Mentor / Comentors: | | Name and Surname | ID | Function |
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| izr. prof. dr. Mojca Ciglarič | 256 | Mentor | | prof. dr. Blaž Zupan | 106 | Comentor |
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| Link to COBISS: | http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537412803) |
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| Institution: | University of Ljubljana |
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| Department: | Faculty of Computer and Information Science |
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| Item ID: | 3785 |
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| Date Deposited: | 10 Feb 2017 13:16 |
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| Last Modified: | 21 Apr 2017 10:35 |
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| URI: | http://eprints.fri.uni-lj.si/id/eprint/3785 |
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