公开(公告)号：US20250090033A1

公开(公告)日：2025-03-20

申请号：US18883902

申请日：2024-09-12

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Suhas BETTAPALLI NAGARAJ ,  Yashas Malur Saidutta ,  Rakshith Sharma Srinivasa ,  Jaejin Cho ,  Ching-Hua Lee ,  Chouchang Yang ,  Yilin Shen ,  Hongxia Jin

IPC: A61B5/021 ,  A61B5/00 ,  A61B5/024 ,  A61B5/318

Abstract: A method for performing cuffless blood pressure (BP) measurement, including: obtaining a first physiological signal and a second physiological signal associated with a user; providing the first physiological signal as an input to a first transformer model; providing the second physiological signal as an input to a second transformer model; providing an output of the first transformer model and an output of the second transformer model as inputs to a third transformer model; providing an output of the third transformer model to at least one BP estimation model; and generating an estimated BP value corresponding to the first physiological signal and the second physiological signal based on an output of the at least one BP estimation model

公开(公告)号：US20240394592A1

公开(公告)日：2024-11-28

申请号：US18434691

申请日：2024-02-06

Applicant: Samsung Electronics Co., Ltd.

Inventor： Rakshith Sharma Srinivasa ,  Jaejin Cho ,  Chouchang Yang ,  Yashas Malur Saidutta ,  Ching-Hua Lee ,  Yilin Shen ,  Hongxia Jin

IPC: G06N20/00

Abstract: A method includes accessing a training dataset having multiple samples, where each sample includes a data point for each of multiple modalities. The method also includes generating, using a first encoder associated with a first modality of the multiple modalities, first modality embeddings for data points of the first modality in the training dataset. The method further includes, for each first modality embedding, determining a similarity metric to other first modality embeddings. The method also includes generating, using a second encoder associated with a second modality of the multiple modalities, second modality embeddings for data points of the second modality in the training dataset. In addition, the method includes training the second encoder based on a contrastive loss function to align the first modality embeddings and the second modality embeddings from different samples of the training dataset, where the contrastive loss function is weighed using the similarity metrics.

公开(公告)号：US20240046946A1

公开(公告)日：2024-02-08

申请号：US18058104

申请日：2022-11-22

Applicant: Samsung Electronics Co., Ltd.

Inventor： Chou-Chang Yang ,  Ching-Hua Lee ,  Rakshith Sharma Srinivasa ,  Yashas Malur Saidutta ,  Yilin Shen ,  Hongxia Jin

IPC: G10L21/0232 ,  G10L15/06 ,  G10L15/02 ,  G10L25/18

CPC classification number: G10L21/0232 ,  G10L15/063 ,  G10L15/02 ,  G10L25/18 ,  G10L2021/02166

Abstract: A method includes obtaining, using at least one processing device, noisy speech signals and extracting, using the at least one processing device, acoustic features from the noisy speech signals. The method also includes receiving, using the at least one processing device, a predicted speech mask from a speech mask prediction model based on a first acoustic feature subset and receiving, using the at least one processing device, a predicted noise mask from a noise mask prediction model based on a second acoustic feature subset. The method further includes providing, using the at least one processing device, predicted speech features determined using the predicted speech mask and predicted noise features determined using the predicted noise mask to a filtering mask prediction model. In addition, the method includes generating, using the at least one processing device, a clean speech signal using a predicted filtering mask output by the filtering mask prediction model.

公开(公告)号：US12260874B2

公开(公告)日：2025-03-25

申请号：US18058104

申请日：2022-11-22

Applicant: Samsung Electronics Co., Ltd.

Inventor： Chou-Chang Yang ,  Ching-Hua Lee ,  Rakshith Sharma Srinivasa ,  Yashas Malur Saidutta ,  Yilin Shen ,  Hongxia Jin

IPC: G10L21/0232 ,  G10L15/02 ,  G10L15/06 ,  G10L21/0216 ,  G10L25/18

Abstract: A method includes obtaining, using at least one processing device, noisy speech signals and extracting, using the at least one processing device, acoustic features from the noisy speech signals. The method also includes receiving, using the at least one processing device, a predicted speech mask from a speech mask prediction model based on a first acoustic feature subset and receiving, using the at least one processing device, a predicted noise mask from a noise mask prediction model based on a second acoustic feature subset. The method further includes providing, using the at least one processing device, predicted speech features determined using the predicted speech mask and predicted noise features determined using the predicted noise mask to a filtering mask prediction model. In addition, the method includes generating, using the at least one processing device, a clean speech signal using a predicted filtering mask output by the filtering mask prediction model.

公开(公告)号：US20250095638A1

公开(公告)日：2025-03-20

申请号：US18891686

申请日：2024-09-20

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Jaejin CHO ,  Rakshith Sharma Srinivasa ,  Chou-chang Yang ,  Yashas Malur Saidutta ,  Ching-Hua Lee ,  Yilin Shen ,  Hongxia Jin

IPC: G10L15/06 ,  G10L15/18 ,  G10L15/183

Abstract: A method includes: receiving one or more training text sentences; generating one or more training vectors based on inputting the one or more training sentences input into a text encoder, the one or more training vectors corresponding to one or more operations that an electronic device is configured to perform; generating one or more speech vectors based on one or more speech utterances input into a speech encoder; generating a similarity matrix that compares each of the one or more training vectors with each of the one or more speech vectors; and updating at least one of the text encoder and the speech encoder based on the similarity matrix.

公开(公告)号：US20240339123A1

公开(公告)日：2024-10-10

申请号：US18470788

申请日：2023-09-20

Applicant: Samsung Electronics Co., Ltd.

Inventor： Chou-Chang Yang ,  Yashas Malur Saidutta ,  Rakshith Sharma Srinivasa ,  Ching-Hua Lee ,  Yilin Shen ,  Hongxia Jin

IPC: G10L21/0232 ,  G10L15/06 ,  G10L15/08 ,  G10L25/18

CPC classification number: G10L21/0232 ,  G10L15/063 ,  G10L15/08 ,  G10L25/18 ,  G10L2015/088

Abstract: A method includes receiving an audio input and generating a noisy time-frequency representation based on the audio input. The method also includes providing the noisy time-frequency representation to a noise management model trained to predict a denoising mask and a signal presence probability (SPP) map indicating a likelihood of a presence of speech. The method further includes determining an enhanced spectrogram using the denoising mask and the noisy time-frequency representation. The method also includes providing the enhanced spectrogram and the SPP map as inputs to a keyword classification model trained to determine a likelihood of a keyword being present in the audio input. In addition, the method includes, responsive to determining that a keyword is in the audio input, transmitting the audio input to a downstream application associated with the keyword.

公开(公告)号：US20240185850A1

公开(公告)日：2024-06-06

申请号：US18352601

申请日：2023-07-14

Applicant: Samsung Electronics Co., Ltd.

Inventor： Rakshith Sharma Srinivasa ,  Yashas Malur Saidutta ,  Ching-Hua Lee ,  Chou-Chang Yang ,  Yilin Shen ,  Hongxia Jin

IPC: G10L15/22 ,  G10L15/02 ,  G10L15/06 ,  G10L15/18 ,  G10L25/78

CPC classification number: G10L15/22 ,  G10L15/02 ,  G10L15/063 ,  G10L15/18 ,  G10L25/78 ,  G10L2015/088 ,  G10L2015/223

Abstract: A method includes extracting, using a keyword detection model, audio features from audio data. The method also includes processing the audio features by a first layer of the keyword detection model configured to predict a first likelihood that the audio data includes speech. The method also includes processing the audio features by a second layer of the keyword detection model configured to predict a second likelihood that the audio data includes keyword-like speech. The method also includes processing the audio features by a third layer of the keyword detection model configured to predict a third likelihood, for each of a plurality of possible keywords, that the audio data includes the keyword. The method also includes identifying a keyword included in the audio data. The method also includes generating instructions to perform an action based at least in part on the identified keyword.