Audio-emotion recognition system using parallel classifiers and audio feature analyzer

Li Wern Chew*, Kah Phooi Seng, Li Minn Ang, Vish Ramakonar, Amalan Gnanasegaran

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

15 Citations (Scopus)

Abstract

Emotion recognition based on an audio signal is an area of active research in the domain of human-computer interaction and effective computing. This paper presents an audio-emotion recognition (AER) system using parallel classifiers and an audio feature analyzer. In the proposed system, audio features such as the pitch and fractional cepstral coefficient are first extracted from the audio signal for analysis. These extracted features are then used to train a radial basis function. Lastly, an audio feature analyzer is used to enhance the performance of the recognition rate. The latest simulation results show that the proposed AER system is able to achieve an emotion recognition rate of 81.67%.

Original languageEnglish
Title of host publicationProceedings - CIMSim 2011
Subtitle of host publication3rd International Conference on Computational Intelligence, Modelling and Simulation
Pages210-215
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2nd International Conference on Computational Intelligence, Modelling and Simulation 2011, CIMSim 2011 - Langkawi, Malaysia
Duration: 20 Sept 201122 Sept 2011

Publication series

NameProceedings - CIMSim 2011: 3rd International Conference on Computational Intelligence, Modelling and Simulation

Conference

Conference2nd International Conference on Computational Intelligence, Modelling and Simulation 2011, CIMSim 2011
Country/TerritoryMalaysia
CityLangkawi
Period20/09/1122/09/11

Keywords

  • Emotion recognition
  • Mel-frequency cepstral coefficients
  • linear discriminant analysis
  • principal component analysis
  • radial basis function

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