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Masato Akagi Professor
School of Information Science、Human Life Design Area

Results 1-20 of about 194

  • 1. Auditory-Inspired End-to-End Speech Emotion Recognition using 3D Convolutional Recurrent Neural Networks based on Spectral Temporal Representation,Zhichao Peng, Zhi Zhu, Masashi Unoki, Jianwu Dang, and Masato Akagi,Proc. ICME2018, San Diego, USA,2018/07/26
  • 2. A Three-Layer Emotion Perception Model for Valence and Arousal-Based Detection from Multilingual Speech,Xingfeng, Li and Masato Akagi,Proc. InterSpeech2018, Hyderabad, India,3643-3647,2018/09/06
  • 3. Unsupervised Singing Voice Separation Based on Robust Principal Component Analysis Exploiting Rank-1 Constraint,Feng Li and Masato Akagi,Proc. EUSIPCO2018, Rome, Italy,1934-1938,2018/09/06
  • 4. Non-parallel Dictionary-based Voice Conversion using Variational Autoencoder with Modulation Spectrum-constrained Training,Ho-Tuan Vu and Akagi Masato,Journal of Signal Processing,22,4,189-192,2018/08/01
  • 5. Contributions of the glottal source and vocal tract cues to emotional vowel perception in the valence-arousal space,Yongwei Li, Junfeng Li, and Masato Akagi,J. Acoust. Soc. Am.,144,2,908-916,2018/08/01
  • 6. Voice conversion for emotional speech: Rule-based synthesis with degree of emotion controllable in dimensional space,Yawen Xue, Yasuhiro Hamada, and Masato Akagi,Speech Communication,102,54-67,2018/09/01
  • 7. Commonalities of glottal sources and vocal tract shapes among speakers in emotional speech,Li, Y., Sakakibara, K-I., Morikawa, D., and Akagi, M.,ISSP2017,2017/10/17
  • 8. Method of Blindly Estimating Speech Transmission Index in Noisy Reverberant Environments,Masashi Unoki, Akikazu Miyazaki, Shota Morita, and Masato Akagi,Journal of Information Hiding and Multimedia Signal Processing, International,8,6,1430-1445,2017/11/01
  • 9. Method of Estimating Signal-to-Noise Ratio Based on Optimal Design for Sub-band Voice Activity Detection,Shota Morita, Xugang Lu, Masashi Unoki and Masato Akagi,Journal of Information Hiding and Multimedia Signal Processing, International,8,6,1446-1459,2017/11/01
  • 10. Feature Selection Method for Real-time Speech Emotion Recognition,Reda Elbarougy and Masato Akagi,O-COCOSDA2017,86-91,2017/11/01
  • 11. Study on Method for Protecting Speech Privacy by Actively Controlling Speech Transmission Index in Simulated Room,Masashi Unoki, Yuta Kashihara, Maori Kobayashi, and Masato Akagi,APSIPA2017,2017/12/14
  • 12. Speech Emotion Recognition Using MPCRNN based on Gammatone auditory Filterbank,Zhichao Peng, Zhi Zhu, Masashi Unoki, Jianwu Dang, and Masato Akagi,APSIPA2017,2017/12/15
  • 13. Acoustical analyses of tendencies of intelligibility in Lombard speech with different background noise levels,Ngo, T. V., Kubo, R., Morikawa, D., and Akagi, M.,Journal of Signal Processing, 21,4,171-174,2017/07/01
  • 14. Automatic Speech Emotion Recognition in Chinese Using a Three-layered Model in Dimensional Approach,Li, X. and Akagi, M.,Proc. NCSP2016, Honolulu, HW, USA,17-20,2016/03/07
  • 15. A study on applying target prediction model to parameterize power envelope of emotional speech,Xue, Y. and Akagi, M.,Proc. NCSP2016, Honolulu, HW, USA,157-160,2016/03/07
  • 16. Study on quality improvement of HMM-based synthesized voices using asymmetric bilinear model,Dinh, T. A, Morikawa, D., and Akagi, M.,Journal of Signal Processing,20,4,205-208,2016/07/01
  • 17. Effects of speaker's and listener's acoustic environments on speech intelligibility and annoyance,Kubo, R, Morikawa, D., and Akagi, M.,Proc. Inter-Noise2016, Hamburg, Germany,171-176,2016/08/22
  • 18. Multilingual Speech Emotion Recognition System Based on a Three-Layer Model,Li, X. and Akagi, M.,Proc. InterSpeech2016, San Francisco,3608-3612,2016/09/12
  • 19. Optimizing Fuzzy Inference Systems for Improving Speech Emotion Recognition,Elbarougy, R. and Akagi, M.,The 2nd International Conference on Advanced Intelligent Systems and Informatics (AISI2016), Cairo, Egypt,85-95,2016/10/24
  • 20. Quality Improvement of HMM-based Synthesized Speech Based on Decomposition of Naturalness and Intelligibility using Non-Negative Matrix Factorization,Dinh, A. T. and Akagi, M.,O-COCOSDA2016, Bali, Indonesia,62-67,2016/10/26

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