Classification of human sounds using support vector machine with psychoacoustic data
- Authors
- Ahmed, Shahzad; Jo, Hyun In; Jeon, Jin Yong
- Issue Date
- Jul-2018
- Publisher
- The International Institute of Acoustic and Vibration (IIAV)
- Keywords
- Human sound classification; Mel frequency cepstral coefficients; Psychoacoustics; Support vector machine
- Citation
- 25th International Congress on Sound and Vibration 2018, ICSV 2018: Hiroshima Calling, v.8, pp.4595 - 4599
- Indexed
- SCOPUS
- Journal Title
- 25th International Congress on Sound and Vibration 2018, ICSV 2018: Hiroshima Calling
- Volume
- 8
- Start Page
- 4595
- End Page
- 4599
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/149672
- Abstract
- This paper presents the classification of human sounds based on support vector machine (SVM) using psychoacoustic data. A scream classification model, with sounds of speech and screams indicating different acoustical characteristics, was investigated. Temporal changes were observed by evaluating the physical characteristics of waveforms and spectrograms with psychoacoustic parameters, including loudness and sharpness. Mel frequency cepstral coefficients were used to identify the spectral energy distribution of screams. Further, a Mel filter bank and frequency band filter were used to extract the high spectral energy, and differentiate between the lower and higher energy spectra. The classification accuracy was improved by combining the SVM with the psy-choacoustic parameters of scream sound.
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