Zobrazeno 1 - 9 of 9 pro vyhledávání: '"Yohei Okato"'
2
User-Selected Object Data Augmentation for 6DOF CNN Localization
Autor: Yohei Okato, Takeru Shiraga, Ken Miyamoto
Publikováno v: ICCE
Automatic content placement is desired for augmented reality (AR) applications. Conventional approaches, such as marker deposition or three-dimensional modeling of all user-selected objects, are time-intensive. We herein propose a framework that is c
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::310e9e73bd2fe3953258bc3e6aeaba33
https://doi.org/10.1109/icce46568.2020.9043083
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3
Teacher-student Deep Clustering for Low-delay Single Channel Speech Separation
Autor: Toshiyuki Hanazawa, Gordon Wichern, Ryo Aihara, Yohei Okato, Jonathan Le Roux
Publikováno v: ICASSP
The recently-proposed deep clustering algorithm introduced significant advances in monaural speaker-independent multi-speaker speech separation. Deep clustering operates on magnitude spectro-grams using bidirectional recurrent networks and K-means cl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bb351a9579fb30690f2942263b384b9e
https://doi.org/10.1109/icassp.2019.8682695
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4
Multichannel NMF with Reduced Computational Complexity for Speech Recognition
Autor: Shingo Uenohara, Toshiyuki Hanazawa, Takanobu Uramoto, Taiki Izumi, Ken'ichi Furuya, Ryo Aihara, Yohei Okato
Publikováno v: APSIPA
In this study, we propose efficient the number of computational iteration method of MNMF for speech recognition. The proposed method initializes and estimates the MNMF algorithm with respect to the estimated spatial correlation matrix reducing the nu
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fbd10b0c18a90d083b3a3061460a5d80
https://doi.org/10.23919/apsipa.2018.8659493
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5
Reducing Computational Complexity of Multichannel Nonnegative Matrix Factorization Using Initial Value Setting for Speech Recognition
Autor: Ken'ichi Furuya, Toshiyuki Hanazawa, Takanobu Uramoto, Yohei Okato, Taiki Izumi, Shingo Uenohara, Ryo Aihara
Publikováno v: Advances in Intelligent Systems and Computing ISBN: 9783319936581
CISIS
In this paper, we propose efficient the number of computational iteration method of MNMF for speech recognition. The proposed method initializes estimates MNMF algorithm with the estimated spatial correlation matrix reduces the number of iteration of
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c761131cf88eafb0d45f3aa2a40c7390
https://doi.org/10.1007/978-3-319-93659-8_82
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9
Conference
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Plný text Recenzováno Digitální knihovna AV ČR
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