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公开(公告)号:US11368652B1
公开(公告)日:2022-06-21
申请号:US17084347
申请日:2020-10-29
Applicant: Amazon Technologies, Inc.
Inventor: Gregory Johnson , Pragyana K. Mishra , Mohammed Khalilia , Wenbin Ouyang , Naveen Sudhakaran Nair
Abstract: Audio content and played frames may be received. The audio content may correspond to first video content. The played frames may be included in the first video content. The first video content may further include a replaced frame. The played frames and the replaced frame may include a face of a person. Location data may also be received that indicates locations of facial features of the face of the person within the replaced frame. A replacement frame may be generated, such as by rendering the facial features in the replacement frame based at least in part on the locations indicated by the location data and positions indicated by a portion of the audio content that is associated with the replaced frame. Second video content may be played including the played frames and the replacement frame. The replacement frame may replace the replaced frame in the second video content.
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公开(公告)号:US11848655B1
公开(公告)日:2023-12-19
申请号:US17475550
申请日:2021-09-15
Applicant: Amazon Technologies, Inc.
Inventor: Mohammed Khalilia , Naveen Sudhakaran Nair
Abstract: Systems, devices, and methods are provided for multi-stem volume equalization, wherein the volume levels of each stem may be adjusted non-uniformly. Audio may be diarized into a plurality of stems, including background noise separate. Mean and variance of the volume levels of the stems may be computed. Each audio stem may be automatically adjusted based on a stem-specific preference that a user may specify. View may adjust actor volume relative to the mean/variance that maintains a relative difference in volume levels between stems.
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公开(公告)号:US11487942B1
公开(公告)日:2022-11-01
申请号:US16437338
申请日:2019-06-11
Applicant: Amazon Technologies, Inc.
Inventor: Thiruvarul Selvan Senthivel , Varun Sembium Varadarajan , Borui Zhang , Tiberiu Mircea Doman , Parminder Bhatia , Arun Kumar Ravi , Mohammed Khalilia , Emine Busra Celikkaya
IPC: G06F16/93 , G06F40/30 , G06F40/295 , G06F16/28 , G06F16/31 , G06N3/04 , G06N3/08 , G06F40/284
Abstract: Techniques for entity and relationship detect from unstructured text as a service are described. A service may receive a request to identify entities within a provided unstructured text element, and the service may segment and tokenize the unstructured text and send the result to multiple services implementing multiple deep machine learning models trained to identify particular entities. The service may send additional requests to an additional service or services implementing additional deep machine learning models to identify relationships between detected attributes and ones of the detected entities. The outputs from all services can be analyzed and consolidated into a single result that identifies the entities, any attributes of the entities, and confidence scores indicating the confidence in each detected entity.
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