Samsung Electronics Co., Ltd.

Sivakumar Balasubramanian

Anil Sunder Yadav

Vijendra R Apsingekar

Srinivasa Rao Ponakala

Gowtham Srinivasan

Aditya Jajodia

Akash Sahoo

A method includes obtaining video content and associated substantially mono audio content. The method also includes determining at least one of a position or a motion trajectory of each of one or more objects detected in the video content and classifying each of the one or more objects into one of multiple object classes. The method further includes separating audio streams within the audio content based on the video content. Each of the audio streams is associated with one of multiple audio sources. The method also includes classifying each of the audio sources into one of the object classes. In addition, the method includes, for each audio source classified into the same object class as one of the one or more objects, distributing the audio stream associated with that audio source into multiple audio channels based on at least one of the position or the motion trajectory of that object.

Three-dimensional (3D) sound rendering with multi-channel audio based on mono audio input

A method includes accessing, using at least one processor of an electronic device, a machine learning model. The machine learning model is a trained student model that is trained using audio samples in a plurality of accent types. The method also includes receiving, using the at least one processor, an audio input from an audio input device. The method further includes providing, using the at least one processor, the audio input to the trained student model. The method also includes receiving, using the at least one processor, an output from the trained student model including frame-level probabilities associated with the audio input. In addition, the method includes instructing, using the at least one processor, at least one action based on the frame-level probabilities associated with the audio input.

System and method for accent-agnostic frame-level wake word detection

A method of generating a trained trigger word detection model includes training an auxiliary model, based on an auxiliary task, to concentrate on one or more utterances and/or learn context of the one or more utterances using generic single word and/or phrase training data; and obtaining a trigger word detection model by retraining one or more final layers of the auxiliary model, which is weighted based on the auxiliary task, based on a trigger word detection task that detects one or more trigger words. The retraining uses training data specific to the one or more trigger words.

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Sunnyvale, CA, United States of America

Sivakumar Balasubramanian