Development of an auditory emotion recognition function using psychoacoustic parameters based on the International Affective Digitized Sounds

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• 作者：Youngimm Choi ; Sungjun Lee ; SungSoo Jung ; In-Mook Choi…
• 关键词：Psychoacoustic parameters ; Emotion recognition ; Auditory emotion recognition ; IADS ; 2 ; Emotional adjectives
• 刊名：Behavior Research Methods
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：47
• 期：4
• 页码：1076-1084
• 全文大小：339 KB
• 参考文献：Adolphs, R., Damasio, H., & Tranel, D. (2002). Neural systems for recognition of emotional prosody: A 3-D lesion study. Emotion, 2, 23-1. doi:10.-037/-528-3542.-.-.-3 CrossRef PubMed
  Belin, P., Fillion-Bilodeau, S., & Gosselin, F. (2008). The Montreal Affective Voices: A validated set of nonverbal affect bursts for research on auditory affective processing. Behavior Research Methods, 40, 531-39. doi:10.-758/?BRM.-0.-.-31 CrossRef PubMed
  Bradley, M. M., & Lang, P. J. (2007). International Affective Digitized Sounds (2nd Edition; IADS-2): Affective ratings of sounds and instruction manual (Technical Report No. B-3). Gainesville, FL: University of Florida, NIMH Center for the Study of Emotion and Attention.
  Calvo, R. A., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 18-7.CrossRef
  Castellano, G., Kessous, L., & Caridakis, G. (2008). Emotion recognition through multiple modalities: Face, body gesture, speech. In C. Peter & R. Beale (Eds.), Affect and emotion in human–computer interaction (Vol. 4868, pp. 92-03). Berlin, Germany: Springer.CrossRef
  Charest, I., Pernet, C. R., Rousselet, G. A., Qui?ones, I., Latinus, M., Fillion-Bilodeau, S., -Belin, P. (2009). Electrophysiological evidence for an early processing of human voices. BMC Neuroscience, 10, 127-38. doi:10.-186/-471-2202-10-127
  Dellaert, F., Polzin, T., & Waibel, A. (1996, October). Recognizing emotion in speech. Paper presented at the Fourth International Conference on Spoken Language (ICSLP 96), Philadelphia, PA.
  Ekman, P. (1973). Cross-cultural studies of facial expression. In P. Ekman (Ed.), Darwin and facial expression: A century of research in review (pp. 169-22). New York, NY: Academic Press.
  Ekman, P., Levenson, R. W., & Friesen, W. V. (1983). Autonomic nervous system activity distinguishes among emotions. Science, 221, 1208-210.CrossRef PubMed
  El Ayadi, M., Kamel, M. S., & Karray, F. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition, 44, 572-87. doi:10.-016/?j.?patcog.-010.-9.-20 CrossRef
  Fastl, H. (2005). Psycho-acoustics and sound quality. In J. Blauert (Ed.), Communication acoustics (pp. 139-62). Berlin, Germany: Springer.CrossRef
  Fu, Z., Lu, G., Ting, K. M., & Zhang, D. (2011). A survey of audio-based music classification and annotation. IEEE Transactions on Multimedia, 13, 303-19.CrossRef
  Gerhard, D. (2003). Audio signal classification: History and current techniques [Working paper]. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada.
  Jack, R. E., Garrod, O. G., & Schyns, P. G. (2014). Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time. Current Biology, 24, 187-92. doi:10.-016/?j.?cub.-013.-1.-64 CrossRef PubMed
  Juslin, P. N., & Laukka, P. (2003). Communication of emotions in vocal expression and music performance: Different channels, same code? Psychological Bulletin, 129, 770-14. doi:10.-037/-033-2909.-29.-.-70 CrossRef PubMed
  Kalat, J. W., & Shiota, M. N. (2007). Emotion. Belmont, CA: Thomson Wadsworth.
  Katsis, C. D., Katertsidis, N., Ganiatsas, G., & Fotiadis, D. I. (2008). Toward emotion recognition in car-racing drivers: A biosignal processing approach. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 38, 502-12.CrossRef
  Kweon, O., & Choi, J. (2009). Preliminary study on human sensibility evaluation of ringtone in mobile phone. Koean Journal of the Science of Emotion and Sensibility, 12, 403-10.
  Lang, P. J. (1994). The varieties of emotional experience: A meditation on James–Lange theory. Psychological Review, 101, 211-21. doi:10.-037/-033-295X.-01.-.-11 CrossRef PubMed
  Lee, C. M., Narayanan, S., & Pieraccini, R. (2001, December). Recognition of negative emotions from the speech signal. Paper presented at the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU -1), Madonna di Campiglio, Italy. doi:10.-109/?ASRU.-001.-034632
  Matos, S., Birring, S. S., Pavord, I. D., & Evans, H. (2006). Detection of cough signals in continuous audio recordings using hidden Markov models. IEEE Transactions on Biomedical Engineering, 53, 1078-083.CrossRef PubMed
  Navas, E., Hernaez, I., & Iker, L. (2006). An objective and subjective study of the role of semantics and prosodic features in building corpora for emotional TTS. IEEE Transactions on Audio, Speech and Language Processing, 14, 1117-127.CrossRef
  Pal, P., Iyer, A. N., & Yantorno, R. E. (2006, May). Emotion detection from infant facial expressions and cries. Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), Toulouse, France.
  Paquette, S., Peretz, I., & Belin, P. (2013). The “Musical Emotional Bursts- A validated set o
• 作者单位：Youngimm Choi (1) (2)
  Sungjun Lee (1)
  SungSoo Jung (1)
  In-Mook Choi (1)
  Yon-Kyu Park (1)
  Chobok Kim (3)
  
  1. Korea Research Institute of Standards and Science, Daejeon, South Korea
  2. Department of Psychology, Chungnam National University, Daejeon, South Korea
  3. Department of Psychology, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, South Korea
  
• 刊物主题：Cognitive Psychology;
• 出版者：Springer US
• ISSN：1554-3528

文摘

The purpose of this study was to develop an auditory emotion recognition function that could determine the emotion caused by sounds coming from the environment in our daily life. For this purpose, sound stimuli from the International Affective Digitized Sounds (IADS-2), a standardized database of sounds intended to evoke emotion, were selected, and four psychoacoustic parameters (i.e., loudness, sharpness, roughness, and fluctuation strength) were extracted from the sounds. Also, by using an emotion adjective scale, 140 college students were tested to measure three basic emotions (happiness, sadness, and negativity). From this discriminant analysis to predict basic emotions from the psychoacoustic parameters of sound, a discriminant function with overall discriminant accuracy of 88.9 % was produced from training data. In order to validate the discriminant function, the same four psychoacoustic parameters were extracted from 46 sound stimuli collected from another database and substituted into the discriminant function. The results showed that an overall discriminant accuracy of 63.04 % was confirmed. Our findings provide the possibility that daily-life sounds, beyond voice and music, can be used in a human–machine interface. Keywords Psychoacoustic parameters Emotion recognition Auditory emotion recognition IADS-2 Emotional adjectives