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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Combining Deep and Hand-Crafted Features for Audio-Based Pain Intensity Classification

verfasst von : Patrick Thiam, Friedhelm Schwenker

Erschienen in: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Verlag: Springer International Publishing

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Abstract

In this work, the classification of pain intensity based on recorded breathing sounds is addressed. A classification approach is proposed and assessed, based on hand-crafted features and spectrograms extracted from the audio recordings. The goal is to use a combination of feature learning (based on deep neural networks) and feature engineering (based on expert knowledge) in order to improve the performance of the classification system. The assessment is performed on the SenseEmotion Database and the experimental results point to the relevance of such a classification approach.

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Vorheriges Kapitel Perceptual Judgments to Detect Computer Generated Forged Faces in Social Media
Nächstes Kapitel Deep Learning Algorithms for Emotion Recognition on Low Power Single Board Computers
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Metadaten
Titel
Combining Deep and Hand-Crafted Features for Audio-Based Pain Intensity Classification
verfasst von
Patrick Thiam
Friedhelm Schwenker
Copyright-Jahr
2019
Buch
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Print ISBN: 978-3-030-20983-4

Electronic ISBN: 978-3-030-20984-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-20984-1_5