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Hisashi Uematsu
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2020 – today
- 2020
- [c8]Yuma Koizumi, Masahiro Yasuda, Shin Murata, Shoichiro Saito, Hisashi Uematsu, Noboru Harada
:
SPIDERnet: Attention Network For One-Shot Anomaly Detection In Sounds. ICASSP 2020: 281-285 - [c7]Masahiro Yasuda, Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Keisuke Imoto:
Sound Event Localization Based on Sound Intensity Vector Refined by Dnn-Based Denoising and Source Separation. ICASSP 2020: 651-655 - [i4]Masahiro Yasuda, Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Keisuke Imoto
:
Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation. CoRR abs/2002.05994 (2020)
2010 – 2019
- 2019
- [j3]Yuma Koizumi
, Shoichiro Saito, Hisashi Uematsu, Yuta Kawachi, Noboru Harada
:
Unsupervised Detection of Anomalous Sound Based on Deep Learning and the Neyman-Pearson Lemma. IEEE ACM Trans. Audio Speech Lang. Process. 27(1): 212-224 (2019) - [c6]Yuma Koizumi, Shin Murata, Noboru Harada
, Shoichiro Saito, Hisashi Uematsu:
SNIPER: Few-shot Learning for Anomaly Detection to Minimize False-negative Rate with Ensured True-positive Rate. ICASSP 2019: 915-919 - [c5]Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Noboru Harada
, Keisuke Imoto
:
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection. WASPAA 2019: 313-317 - [i3]Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Noboru Harada, Keisuke Imoto:
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection. CoRR abs/1908.03299 (2019) - [i2]Masahiro Yasuda, Yuma Koizumi, Luca Mazzon, Shoichiro Saito, Hisashi Uematsu:
DOA Estimation by DNN-based Denoising and Dereverberation from Sound Intensity Vector. CoRR abs/1910.04415 (2019) - 2018
- [j2]Kenta Niwa
, Yusuke Hioka, Hisashi Uematsu:
Efficient Audio Rendering Using Angular Region-Wise Source Enhancement for 360° Video. IEEE Trans. Multim. 20(11): 2871-2881 (2018) - [i1]Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Yuta Kawachi, Noboru Harada:
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma. CoRR abs/1810.09133 (2018) - 2017
- [c4]Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Noboru Harada
:
Optimizing acoustic feature extractor for anomalous sound detection based on Neyman-Pearson lemma. EUSIPCO 2017: 698-702 - 2016
- [c3]Kenta Niwa, Yuma Koizumi, Kazunori Kobayashi, Hisashi Uematsu:
Binaural sound generation corresponding to omnidirectional video view using angular region-wise source enhancement. ICASSP 2016: 2852-2856 - 2014
- [j1]Shoichi Koyama
, Ken'ichi Furuya
, Hisashi Uematsu, Yusuke Hiwasaki, Yoichi Haneda:
Real-Time Sound Field Transmission System by Using Wave Field Reconstruction Filter and Its Evaluation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 97-A(9): 1840-1848 (2014) - 2013
- [c2]Keisuke Imoto, Suehiro Shimauchi, Hisashi Uematsu, Hitoshi Ohmuro:
User activity estimation method based on probabilistic generative model of acoustic event sequence with user activity and its subordinate categories. INTERSPEECH 2013: 2609-2613 - [c1]Keisuke Imoto
, Yasunori Ohishi, Hisashi Uematsu, Hitoshi Ohmuro:
Acoustic scene analysis based on latent acoustic topic and event allocation. MLSP 2013: 1-6
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