Autonomous semantic labeling of physical locations

    公开(公告)号:US10219129B2

    公开(公告)日:2019-02-26

    申请号:US15722872

    申请日:2017-10-02

    Abstract: A portable electronic device may generate a (RF) radio frequency fingerprint that includes information representative of at least a portion of RF signals received at a given physical location. The RF fingerprint may include, for example, a unique identifier and a signal strength that are both logically associated with at least a portion of the received RF signals. The portable electronic device may also receive data representative of a number of environmental parameters about the portable electronic device. These environmental parameters may be measured using sensors carried by the portable electronic device. Considered in combination, these environmental parameters provide an environmental signature for a given location. When combined into a data cluster, the RF fingerprint and the environmental signature may provide an indication of the physical subdivision where the portable electronic device is located. The portable electronic device may then generate a proposed semantic label for the physical subdivision.

    Autonomous semantic labeling of physical locations

    公开(公告)号:US09781575B1

    公开(公告)日:2017-10-03

    申请号:US15084799

    申请日:2016-03-30

    CPC classification number: H04W4/30 H04L67/303 H04W4/023 H04W4/04 H04W4/70 H04W4/80

    Abstract: A portable electronic device may generate a (RF) radio frequency fingerprint that includes information representative of at least a portion of RF signals received at a given physical location. The RF fingerprint may include, for example, a unique identifier and a signal strength that are both logically associated with at least a portion of the received RF signals. The portable electronic device may also receive data representative of a number of environmental parameters about the portable electronic device. These environmental parameters may be measured using sensors carried by the portable electronic device. Considered in combination, these environmental parameters provide an environmental signature for a given location. When combined into a data cluster, the RF fingerprint and the environmental signature may provide an indication of the physical subdivision where the portable electronic device is located. The portable electronic device may then generate a proposed semantic label for the physical subdivision.

    ADAPTING HEARING AIDS TO DIFFERENT ENVIRONMENTS

    公开(公告)号:US20180213339A1

    公开(公告)日:2018-07-26

    申请号:US15413012

    申请日:2017-01-23

    Abstract: In some embodiments, the disclosed subject matter involves a system and method relating to improving the user experience of hearing, using an adaptable or adjustable hearing aid that takes environmental conditions into account when changing modes. A local server or gateway or cloud service iteratively analyzes the audio environment and feedback from the user to automatically change settings and mode of the user's hearing aid to improve hearing. Information from other users in similar audio environments may be used to assist in mode changes. Information about the audio environment, hearing aid settings/mode and user feedback may be correlated for future use by the user, or crowdsourced for other users, the hearing aid manufacturer or audiologist. Other embodiments are described and claimed.

    Context-aware enrollment for text independent speaker recognition

    公开(公告)号:US10339935B2

    公开(公告)日:2019-07-02

    申请号:US15626828

    申请日:2017-06-19

    Abstract: Techniques are provided for training of a text independent (TI) speaker recognition (SR) model. A methodology implementing the techniques according to an embodiment includes measuring context data associated with collected TI speech utterances from a user and identifying the user based on received identity measurements. The method further includes performing a speech quality analysis and a speaker state analysis based on the utterances, and evaluating a training merit value of the utterances, based on the speech quality analysis and the speaker state analysis. If the training merit value exceeds a threshold value, the utterances are stored as training data in a training database. The database is indexed by the user identity and the context data. The method further includes determining whether the stored training data has achieved a sufficiency level for enrollment of a TI SR model, and training the TI SR model for the identified user and context.

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