公开(公告)号：US12266379B2

公开(公告)日：2025-04-01

申请号：US17937765

申请日：2022-10-03

Applicant: QUALCOMM Incorporated

Inventor： Byeonggeun Kim ,  Seunghan Yang ,  Hyunsin Park ,  Juntae Lee ,  Simyung Chang

IPC: G10L21/034 ,  G10L17/04 ,  G10L17/18 ,  G10L25/30 ,  G10L25/51

Abstract: Techniques and apparatus for training a neural network to classify audio into one of a plurality of categories and using such a trained neural network. An example method generally includes receiving a data set including a plurality of audio samples. A relaxed feature-normalized data set is generated by normalizing each audio sample of the plurality of audio samples. A neural network is trained to classify audio into one of a plurality of categories based on the relaxed feature-normalized data set, and the trained neural network is deployed.

公开(公告)号：US11908155B2

公开(公告)日：2024-02-20

申请号：US17203607

申请日：2021-03-16

Applicant: QUALCOMM Incorporated

Inventor： John Yang ,  Yash Sanjay Bhalgat ,  Fatih Murat Porikli ,  Simyung Chang

IPC: G06F18/213 ,  G06N20/00 ,  G06T7/70

CPC classification number: G06T7/70 ,  G06F18/213 ,  G06N20/00 ,  G06T2207/20081

Abstract: Certain aspects of the present disclosure provide a method, including: processing input data with a feature extraction stage of a machine learning model to generate a feature map; applying an attention map to the feature map to generate an augmented feature map; processing the augmented feature map with a refinement stage of the machine learning model to generate a refined feature map; processing the refined feature map with a first regression stage of the machine learning model to generate multi-dimensional task output data; and processing the refined feature data with an attention stage of the machine learning model to generate an updated attention map.

公开(公告)号：US20240290332A1

公开(公告)日：2024-08-29

申请号：US18355055

申请日：2023-07-19

Applicant: QUALCOMM Incorporated

Inventor： Kyuhong Shim ,  Jinkyu Lee ,  Simyung Chang ,  Kyu Woong Hwang

IPC: G10L15/22 ,  G10L15/18 ,  G10L15/26

CPC classification number: G10L15/22 ,  G10L15/18 ,  G10L15/26 ,  G10L2015/223

Abstract: An example device includes memory configured to store a speech signal representative of speech and a streaming model. The streaming model includes an on-device, real-time streaming model. The device includes one or more processors implemented in circuitry coupled to the memory. The one or more processors are configured to determine one or more words in the speech signal based on one or more transfers of learned knowledge from a non-streaming model to the streaming model. The one or more processors are also configured to take an action based on the determined one or more words.

公开(公告)号：US12019641B2

公开(公告)日：2024-06-25

申请号：US18153899

申请日：2023-01-12

Applicant: QUALCOMM Incorporated

Inventor： Byeonggeun Kim ,  Juntae Lee ,  Simyung Chang

IPC: G06F7/00 ,  G06F16/2458 ,  G06F16/28

CPC classification number: G06F16/2462 ,  G06F16/285

Abstract: Systems and techniques are provided for processing one or more data samples. For example, a neural network classifier can be trained to perform few-shot open-set recognition (FSOSR) based on a task-agnostic open-set prototype. A process can include determining one or more prototype representations for each class included in a plurality of support samples. A task-agnostic open-set prototype representation can be determined, in a same learned metric space as the one or more prototype representations. One or more distance metrics can be determined for each query sample of one or more query samples, based on the one or more prototype representations and the task-agnostic open-set prototype representation. Based on the one or more distance metrics, each query sample can be classified into one of classes associated with the one or more prototype representations or an open-set class associated with the task-agnostic open-set prototype representation.

公开(公告)号：US11798204B2

公开(公告)日：2023-10-24

申请号：US17685278

申请日：2022-03-02

Applicant: QUALCOMM Incorporated

Inventor： Hyunsin Park ,  Juntae Lee ,  Simyung Chang ,  Byeonggeun Kim ,  Jaewon Choi ,  Kyu Woong Hwang

IPC: G06T11/00 ,  G06V40/16 ,  G06F3/01 ,  G06V40/18

CPC classification number: G06T11/00 ,  G06F3/013 ,  G06V40/174 ,  G06V40/18

Abstract: Imaging systems and techniques are described. An imaging system receives image data representing at least a portion (e.g., a face) of a first user as captured by a first image sensor. The imaging system identifies that a gaze of the first user as represented in the image data is directed toward a displayed representation of at least a portion (e.g., a face) of a second user. The imaging system identifies an arrangement of representations of users for output. The imaging system generates modified image data based on the gaze and the arrangement at least in part by modifying the image data to modify at least the portion of the first user in the image data to be visually directed toward a direction corresponding to the second user based on the gaze and the arrangement. The imaging system outputs the modified image data arranged according to the arrangement.

公开(公告)号：US20220405547A1

公开(公告)日：2022-12-22

申请号：US17807479

申请日：2022-06-17

Applicant: QUALCOMM Incorporated

Inventor： Byeonggeun KIM ,  Simyung Chang ,  Jangho Kim ,  Seunghan Yang ,  Kyu Woong Hwang

IPC: G06N3/04 ,  G06K9/62

Abstract: Certain aspects of the present disclosure provide techniques for residual normalization. A first tensor comprising a frequency dimension and a temporal dimension is accessed. A second tensor is generated by applying a frequency-based instance normalization operation to the first tensor, comprising, for each respective frequency bin in the frequency dimension, computing a respective frequency-specific mean of the first tensor. A third tensor is generated by: scaling the first tensor by a scale value, and aggregating the scaled first tensor and the second tensor. The third tensor is provided as input to a layer of a neural network.

公开(公告)号：US20220309344A1

公开(公告)日：2022-09-29

申请号：US17656621

申请日：2022-03-25

Applicant: QUALCOMM Incorporated

Inventor： Byeonggeun KIM ,  Simyung Chang ,  Jinkyu Lee ,  Dooyong Sung

IPC: G06N3/08 ,  G06F17/15

Abstract: Certain aspects of the present disclosure provide techniques for efficient broadcasted residual machine learning. An input tensor comprising a frequency dimension and a temporal dimension is received, and the input tensor is processed with a first convolution operation to generate a multidimensional intermediate feature map comprising the frequency dimension and the temporal dimension. The multidimensional intermediate feature map is converted to a one-dimensional intermediate feature map in the temporal dimension using a frequency dimension reduction operation, and the one-dimensional intermediate feature map is processed using a second convolution operation to generate a temporal feature map. The temporal feature map is expanded to the frequency dimension using a broadcasting operation to generate a multidimensional output feature map, and the multidimensional output feature map is augmented with the multidimensional intermediate feature map via a first residual connection.