Music emotion recognition using deep Gaussian process

Sih Huei Chen, Yuan Shan Lee, Wen Chi Hsieh, Jia Ching Wang

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

19 Scopus citations

Abstract

Music is a powerful force that evokes human emotions. Several investigations of music emotion recognition (MER) have been conducted in recent years. This paper proposes a system for detecting emotion in music that is based on a deep Gaussian process (GP). The system consists of two parts-feature extraction and classification. In the feature extraction part, five types of features that are associated with emotions are selected for representing the music signal; these are rhythm, dynamics, timbre, pitch and tonality. A music clip is decomposed into frames and these features are extracted from each frame. Next, statistical values, such as mean and standard deviation, of frame-based features are calculated to generate a 38-dimensional feature vector. In the classification part, a deep GP is utilized for emotion recognition. We treat classification problem from the perspective of regression. Finally, 9 classes of emotion are categorized by 9 one-versus-all classifiers. The experimental results demonstrate that the proposed system performs well in emotion recognition.

Original languageEnglish
Title of host publication2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages495-498
Number of pages4
ISBN (Electronic)9789881476807
DOIs
StatePublished - 19 Feb 2016
Event2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong
Duration: 16 Dec 201519 Dec 2015

Publication series

Name2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

Conference

Conference2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Country/TerritoryHong Kong
CityHong Kong
Period16/12/1519/12/15

Keywords

  • classification
  • deep Gaussian process
  • feature extraction
  • Music emotion recognition

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