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公开(公告)号：US20210361227A1
公开(公告)日：2021-11-25
申请号：US17045318
申请日：2018-05-04
Applicant: Google LLC
Inventor： Katherine Chou , Michael Dwight Howell , Kasumi Widner , Ryan Rifkin , Henry George Wei , Daniel Ellis , Alvin Rajkomar , Aren Jansen , David Michael Parish , Michael Philip Brenner
Abstract: The present disclosure provides systems and methods that generating health diagnostic information from an audio recording. A computing system can include a machine-learned health model comprising that includes a sound model trained to receive data descriptive of a patient audio recording and output sound description data. The computing system can include a diagnostic model trained to receive the sound description data and output a diagnostic score. The computing system can include at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score.
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公开(公告)号：US20200349921A1
公开(公告)日：2020-11-05
申请号：US16758564
申请日：2018-10-26
Applicant: Google LLC
Inventor： Aren Jansen , Manoj Plakal , Richard Channing Moore , Shawn Hershey , Ratheet Pandya , Ryan Rifkin , Jiayang Liu , Daniel Ellis
Abstract: Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.
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公开(公告)号：US11335328B2
公开(公告)日：2022-05-17
申请号：US16758564
申请日：2018-10-26
Applicant: Google LLC
Inventor： Aren Jansen , Manoj Plakal , Richard Channing Moore , Shawn Hershey , Ratheet Pandya , Ryan Rifkin , Jiayang Liu , Daniel Ellis
Abstract: Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.
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Patent Agency Ranking
公开(公告)号：US12070323B2
公开(公告)日：2024-08-27
申请号：US17045318
申请日：2018-05-04
Applicant: Google LLC
Inventor： Katherine Chou , Michael Dwight Howell , Kasumi Widner , Ryan Rifkin , Henry George Wei , Daniel Ellis , Alvin Rajkomar , Aren Jansen , David Michael Parish , Michael Philip Brenner
CPC classification number: A61B5/4803 , A61B5/7264 , A61B5/7275 , G06N20/00 , G10L25/63 , G10L25/66
Abstract: The present disclosure provides systems and methods that generating health diagnostic information from an audio recording. A computing system can include a machine-learned health model comprising that includes a sound model trained to receive data descriptive of a patient audio recording and output sound description data. The computing system can include a diagnostic model trained to receive the sound description data and output a diagnostic score. The computing system can include at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score.
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公开(公告)号：US20180197533A1
公开(公告)日：2018-07-12
申请号：US15839499
申请日：2017-12-12
Applicant: GOOGLE LLC
Inventor： Richard Lyon , Christopher Hughes , Yuxuan Wang , Ryan Rifkin , Pascal Getreuer
Abstract: The various implementations described herein include methods, devices, and systems for recognizing speech, such as user commands. In one aspect, a method includes: (1) receiving audio input data via the one or more microphones; (2) generating a plurality of energy channels for the audio input data; (3) generating a feature vector by performing a per-channel normalization to each channel of the plurality of energy channels; and (4) obtaining recognized speech from the audio input utilizing the feature vector.
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公开(公告)号：US20240366148A1
公开(公告)日：2024-11-07
申请号：US18773046
申请日：2024-07-15
Applicant: Google LLC
Inventor： Katherine Chou , Michael Dwight Howell , Kasumi Widner , Ryan Rifkin , Henry George Wei , Daniel Ellis , Alvin Rajkomar , Aren Jansen , David Michael Parish , Michael Philip Brenner
Abstract: The present disclosure provides systems and methods that generating health diagnostic information from an audio recording. A computing system can include a machine-learned health model comprising that includes a sound model trained to receive data descriptive of a patient audio recording and output sound description data. The computing system can include a diagnostic model trained to receive the sound description data and output a diagnostic score. The computing system can include at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score.
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公开(公告)号：US20200250515A1
公开(公告)日：2020-08-06
申请号：US16624949
申请日：2018-07-06
Applicant: Google LLC
Inventor： Ryan Rifkin , Ying Xiao , Shankar Krishnan
Abstract: Generally, the present disclosure is directed to systems and methods for improved optimization of machine-learned models. In particular, the present disclosure provides stochastic optimization algorithms that are both faster than widely used algorithms for fixed amounts of computation, and are also able to scale up substantially better as more computational resources become available. The stochastic optimization algorithms can be used with large batch sizes. As an example, in some implementations, the systems and methods of the present disclosure can implicitly compute the inverse hessian of each mini-batch of training data to produce descent directions.
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公开(公告)号：US10672387B2
公开(公告)日：2020-06-02
申请号：US15839499
申请日：2017-12-12
Applicant: GOOGLE LLC
Inventor： Richard Lyon , Christopher Hughes , Yuxuan Wang , Ryan Rifkin , Pascal Getreuer
Abstract: The various implementations described herein include methods, devices, and systems for recognizing speech, such as user commands. In one aspect, a method includes: (1) receiving audio input data via the one or more microphones; (2) generating a plurality of energy channels for the audio input data; (3) generating a feature vector by performing a per-channel normalization to each channel of the plurality of energy channels; and (4) obtaining recognized speech from the audio input utilizing the feature vector.
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