Invention Application
- Patent Title: NOISE SPATIAL COVARIANCE MATRIX ESTIMATION APPARATUS, NOISE SPATIAL COVARIANCE MATRIX ESTIMATION METHOD, AND PROGRAM
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Application No.: US17437701Application Date: 2020-02-28
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Publication No.: US20220130406A1Publication Date: 2022-04-28
- Inventor: Tomohiro NAKATANI , Marc DELCROIX , Keisuke KINOSHITA , Shoko ARAKI , Yuki KUBO
- Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
- Applicant Address: JP Tokyo
- Assignee: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
- Current Assignee: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
- Current Assignee Address: JP Tokyo
- Priority: JP2019-045649 20190313
- International Application: PCT/JP2020/008216 WO 20200228
- Main IPC: G10L21/0232
- IPC: G10L21/0232 ; G10L21/028 ; G10K11/175

Abstract:
A time-variant noise spatial covariance matrix is estimated effectively. Using time-frequency-divided observation signals based on observation signals acquired by collecting acoustic signals emitted from one or a plurality of sound sources and mask information expressing the occupancy probability of a component of each of the time-frequency-divided observation signals that corresponds to each noise source, a time-independent first noise spatial covariance matrix corresponding to the time-frequency-divided observation signals and the mask information belonging to a long time interval is acquired for each noise source. Further, using the mask information of each of a plurality of different short time intervals, a mixture weight corresponding to each noise source in each short time interval is acquired. Furthermore, a time-variant third noise spatial covariance matrix is acquired, the third noise spatial covariance matrix being based on a time-variant second noise spatial covariance matrix, which corresponds to the time-frequency-divided observation signals and the mask information belonging to each short time interval and relates to noise formed by adding together all of the noise sources, and a weighted sum of the first noise spatial covariance matrices with the mixture weights of the respective short time intervals.
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