research-article

Functional-Based Acoustic Group Feature Selection for Automatic Recognition of Eating Condition

Author:

Dara PirAuthors Info & Claims

ICMI '18: Proceedings of the 20th ACM International Conference on Multimodal Interaction

Pages 579 - 583

https://doi.org/10.1145/3242969.3243682

Published: 02 October 2018 Publication History

Abstract

This paper presents the novel Functional-based acoustic Group Feature Selection (FGFS) method for automatic eating condition recognition addressed in the ICMI 2018 Eating Analysis and Tracking Challenge's Food-type Sub-Challenge. The Food-type Sub-Challenge employs the audiovisual iHEARu-EAT database and attempts to classify which of six food types, or none, is being consumed by subjects while speaking. The approach proposed by the FGFS method uses the audio mode and considers the acoustic feature space in groups rather than individually. Each group is comprised of acoustic features generated by the application of a statistical functional to a specified set of the low-level descriptors of the audio data. The FGFS method provides information about the degree of relevance of the statistical functionals to the task. In addition, the partitioning of features into groups allows for more rapid processing of the official Sub-Challenge's large acoustic feature set. The FGFS-based system achieves 2.8% relative Unweighted Average Recall performance improvement over the official Food-type Sub-Challenge baseline on iHEARu-EAT test data.

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Cited By

Yan ZChen DTeng ZWang DLi Y(2020)SMOPredT4SE: An Effective Prediction of Bacterial Type IV Secreted Effectors Using SVM Training With SMOIEEE Access10.1109/ACCESS.2020.29710918(25570-25578)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2971091
Schuller DSchuller B(2019)The Challenge of Automatic Eating Behaviour Analysis and TrackingRecent Advances in Intelligent Assistive Technologies: Paradigms and Applications10.1007/978-3-030-30817-9_8(187-204)Online publication date: 8-Nov-2019
https://doi.org/10.1007/978-3-030-30817-9_8

Index Terms

Functional-Based Acoustic Group Feature Selection for Automatic Recognition of Eating Condition
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Speech / audio search

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Published In

cover image ACM Other conferences

ICMI '18: Proceedings of the 20th ACM International Conference on Multimodal Interaction

October 2018

687 pages

ISBN:9781450356923

DOI:10.1145/3242969

General Chairs:
Sidney K. D'Mello
University of Illinois, USA
,
Panayiotis (Panos) Georgiou
University of Southern California, USA
,
Stefan Scherer
University of Southern California, USA
,
Program Chairs:
Emily Mower Provost
University of Michigan, USA
,
Mohammad Soleymani
University of Southern California, USA
,
Marcelo Worsley
Northwestern University, USA

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 02 October 2018

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ICMI '18: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 16 - 20, 2018

CO, Boulder, USA

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ICMI '18 Paper Acceptance Rate 63 of 149 submissions, 42%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Yan ZChen DTeng ZWang DLi Y(2020)SMOPredT4SE: An Effective Prediction of Bacterial Type IV Secreted Effectors Using SVM Training With SMOIEEE Access10.1109/ACCESS.2020.29710918(25570-25578)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2971091
Schuller DSchuller B(2019)The Challenge of Automatic Eating Behaviour Analysis and TrackingRecent Advances in Intelligent Assistive Technologies: Paradigms and Applications10.1007/978-3-030-30817-9_8(187-204)Online publication date: 8-Nov-2019
https://doi.org/10.1007/978-3-030-30817-9_8

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