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Title:
SOUND SOURCE EMPHASIS LEARNING DEVICE, SOUND SOURCE EMPHASIS DEVICE, SOUND SOURCE EMPHASIS LEARNING METHOD, PROGRAM, AND SIGNAL PROCESSING LEARNING DEVICE
Document Type and Number:
Japanese Patent JP2018031910
Kind Code:
A
Abstract:
PROBLEM TO BE SOLVED: To provide a sound source emphasis learning device configured to learn a network parameter for sound source emphasis in an accurate and stable manner.SOLUTION: A previous learning section includes: a target sound non-negative auto-encoder calculation part 126 for calculating a target sound spectrum characteristic vector by modeling a spectral characteristic of a target sound from target sound BF output power that is generated from frequency domain mixed sound learning data; a noise non-negative auto-encoder calculation part 127 for calculating a noise spectral characteristic vector by modeling a spectral characteristic of noise from noise BF output power that is generated from the frequency domain mixed sound learning data; a complementary subtraction neural network calculation part 128 for calculating an estimated target sound PSD and an estimated noise PSD from the target sound spectral characteristic vector and the noise spectral characteristic vector; and a network parameter optimization part 129 for optimizing a network parameter by using the estimated target sound PSD, the estimated noise PSD, a target sound PSD and a noise PSD.SELECTED DRAWING: Figure 2

Inventors:
NIWA KENTA
KOBAYASHI KAZUNORI
KOIZUMI YUMA
KAWASE TOMOKO
Application Number:
JP2016164726A
Publication Date:
March 01, 2018
Filing Date:
August 25, 2016
Export Citation:
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Assignee:
NIPPON TELEGRAPH & TELEPHONE
International Classes:
G10L21/0272; G10L21/0308
Domestic Patent References:
JP2016143042A2016-08-08
JP2010541010A2010-12-24
Foreign References:
US20160071526A12016-03-10
US20150095026A12015-04-02
Other References:
丹羽健太他: "相互情報量増大型受音とDNN回帰に基づく遠距離収音技術に関する検討", 日本音響学会2016年春季研究発表会講演論文集[CD−ROM], JPN6019028738, February 2016 (2016-02-01), pages 551 - 554, ISSN: 0004083097
Attorney, Agent or Firm:
Naoki Nakao
Yukio Nakamura
Yoshimura Munehiro