Amazon Technologies, Inc.

Samsung Electronics Co., Ltd.

Netapp, Inc.

Sandeep Joshi

Muhammad Raffay Hamid

Mayank Sharma

Tamojit Chatterjee

Ashok Kumar Reddy Chavva

Anusha Gunturu

Sudip Kumar Panda

Ankit Mathur

In an embodiment, a method of a base station (BS) for managing an ISI in a cellular network is disclosed. The method includes receiving at least one UE-Capability information element from a UE comprising a list of CP lengths and a list of SCSs, determining a plurality of parameters associated with the UE based on the list of CP lengths and the list of SCSs, calculating at least one of a first custom CP length and a first SCS based on the plurality of parameters from the list of CP lengths and the list of SCSs, and transmitting, to the UE, a response message indicating that at least one of the first custom CP length and the first SCS is selected for managing the ISI.

Method and system for managing an intersymbol interference in an ultra-high frequency cellular network

Systems, methods, and computer-readable media are disclosed for language-agnostic subtitle drift detection and correction. A method may include determining subtitles and/or captions from media content (e.g., videos), the subtitles and/or captions corresponding to dialog in the media content. The subtitles may be broken up into segments which may be analyzed to determine a likelihood of drift (e.g., a likelihood that the subtitles are out of synchronization with the dialog in the media content) for each segment. For segments with a high likelihood of drift, the subtitles may be incrementally adjusted to determine an adjustment that eliminates and/or reduces the amount of drift, and the drift in the segment may be corrected based on the drift amount detected. A linear regression model and/or human blocks determined by human operators may be used to otherwise optimize drift correction.

Language agnostic drift correction

Systems, methods, and computer-readable media are disclosed for systems and methods for language agnostic automated voice activity detection. Example methods may include determining an audio file associated with video content, generating audio segments using the audio file, the audio segments including a first segment and a second segment, and determining that the first segment includes first voice activity. Methods may include determining that the second segment comprises second voice activity, determining that voice activity is present between a first timestamp associated with the first segment and a second timestamp associated with the second segment, and generating text data representing the voice activity that is present between the first timestamp and the second timestamp.

Language agnostic automated voice activity detection

Systems, methods, and computer-readable media are disclosed for language-agnostic subtitle drift detection and correction. A method may include determining subtitles and/or captions from media content (e.g., videos), the subtitles and/or captions corresponding to dialog in the media content. The subtitles may be broken up into segments which may be analyzed to determine a likelihood of drift (e.g., a likelihood that the subtitles are out of synchronization with the dialog in the media content) for each segment. For segments with a high likelihood of drift, the subtitles may be incrementally adjusted to determine an adjustment that eliminates and/or reduces the amount of drift and the drift in the segment may be corrected based on the drift amount detected. A linear regression model and/or human blocks determined by human operators may be used to otherwise optimize drift correction.

Systems, methods, and computer-readable media are disclosed for systems and methods for language agnostic automated voice activity detection. Example methods may include determining an audio file associated with video content, generating a number of audio segments using the audio file, the plurality of audio segments including a first segment and a second segment, where the first segment and the second segment are consecutive segments. Example methods may include determining, using a Gated Recurrent Unit neural network, that the first segment includes first voice activity, determining, using the Gated Recurrent Unit neural network, that the second segment includes second voice activity, and determining that voice activity is present between a first timestamp associated with the first segment and a second timestamp associated with the second segment.

Devices, systems, and methods are provided for language-agnostic subtitle drift detection and localization. A method may include extracting audio from video, dividing the audio into overlapping blocks, and determining the probabilities of overlapping portions of the blocks, the probabilities indicating a presence of voice data represented by the audio in the blocks. The method may generate machine blocks using overlapping portions of blocks where voice data is present, and may map the machine blocks to corresponding blocks indicating that subtitles are available for the video. For mapped blocks, the method may include determining features such as when subtitles are available without voice audio, when voice audio is available without subtitles, and when voice audio and subtitles both are available. Using the features, the method may include determining the probability that the video includes subtitle drift, and the method may include analyzing the video to localize where the subtitle drift occurs.

Language-agnostic subtitle drift detection and localization

A first request to execute a first task is received from a first module in a first address space and by a second module in a second address space. The first task is placed into a task queue for execution in the second address space. Pending responses not yet returned to the first module that are results of execution for other tasks in the second address space are extracted by the second module from a response queue. Requests for the other tasks were previously sent by the first module to the second module for execution in the second address space. The pending responses are compounded. The pending responses and a return value for acknowledgement the first request to execute the first task are combined, by the second module into a combined communication. The combined communication is transmitted by the second module to the first module in the first address space.

Growing community of inventors

Bengaluru, India

Sandeep Joshi