| CPC G10L 25/87 (2013.01) [G10L 21/14 (2013.01); G10L 25/27 (2013.01)] | 19 Claims |
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1. A system for detecting gunshots, comprising:
a computing device comprising:
a device housing configured to removably couple to a light fixture, wherein a photoelectric cell of the light fixture is coupled to an upper surface of the device housing;
a microphone inside or mounted to the device housing; and
a processor inside the device housing and electrically coupled to the microphone, the processor configured to:
receive a set of audio data from the microphone;
execute a first machine learning model using a segment of the set of audio data comprising a first plurality of sounds as input to determine whether the segment of the set of audio data is likely to contain a sound of a gunshot; and
responsive to determining the segment of the set of audio data is likely to contain the sound of the gunshot based on the execution of the first machine learning model, transmit the segment of the set of audio data to a remote computing device; and
the remote computing device comprising:
a remote processor configured to:
receive the segment of audio data from the computing device responsive to the processor determining the segment of the set of audio data is likely to contain the sound of a gunshot;
responsive to receiving a plurality of segments of audio data each comprising a second plurality of sounds and corresponding to the gunshot received from a plurality of computing devices, the plurality of segments of audio data including the segment of audio data from the computing device, iteratively execute, for each segment of audio data of each computing device, a second machine learning model using the segment of audio data received from the computing device to output a plurality of timestamps of the gunshot for the computing device; and
determine a location of the gunshot based on a device location and only a first timestamp of the plurality of timestamps for each of the plurality of segments of audio data, each first timestamp for each segment of audio data selected based on the first timestamp corresponding to an earliest time of the plurality of timestamps for the segment of audio data.
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6. A method, comprising:
receiving, by a processor of an edge recording device, a first set of audio data from a microphone inside or mounted to a housing of the edge recording device, the first set of audio data comprising a sound recording;
executing, by the processor, a first machine learning model using a first segment of the first set of audio data as input to determine the first segment of audio data comprising a first plurality of sounds is likely to contain a sound of a gunshot;
responsive to determining the first segment of the first set of audio data is likely to contain the sound of the gunshot, transmitting, by the processor, the first segment of audio data to a first remote processor;
receiving, by the first remote processor, the first segment of audio data as a segment of audio data of a plurality of segments of audio data received from a plurality of edge recording devices, each of the plurality of segments of audio data transmitted to the first remote processor in response to a determination the segment is likely to contain the sound of the gunshot;
responsive to receiving the plurality of segments of audio data from the plurality of edge recording devices, iteratively executing, by the first remote processor for each segment of audio data of each edge recording device, a second machine learning model using the segment of audio data received from the edge recording device to output a plurality of timestamps of the gunshot for the edge recording device;
determining a location of the gunshot based on a device location and only a first timestamp of the plurality of timestamps for each of the plurality of segments of audio data, each first timestamp for each segment of audio data selected based on the first timestamp corresponding to an earliest time of the plurality of timestamps for the segment of audio data; and
and
transmitting, by the first remote processor, an indication of the location to a second remote processor of a second remote device.
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9. A system, comprising
a first remote processor of a first remote computing device remote from a set of edge recording devices, the first remote processor coupled to a first remote non-transitory memory of the first remote computing device, wherein the first remote processor is configured to:
receive a segment of audio data comprising a plurality of sounds from each of a subset of the set of edge recording devices, each segment of audio data transmitted to the first remote processor in response to a determination that segment of audio data is likely to contain a sound of a gunshot, the subset comprising a plurality of edge recording devices;
responsive to receiving the segment of audio data from each of the subset of edge recording devices, iteratively execute, for each of the subset of edge recording devices, a machine learning model trained to use the segment of audio data received from the edge recording device to output a plurality of timestamps of the gunshot for the edge recording device;
determine a location of the gunshot based on a device location and only a first timestamp of the plurality of timestamps for each of the plurality of segments of audio data, each first timestamp for each segment of audio data selected based on the first timestamp corresponding to an earliest time of the plurality of timestamps for the segment of audio data;
and
transmit an indication of the location to a second remote processor of a second remote computing device.
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19. A system comprising:
a computing device comprising:
a device housing configured to removably couple to a light fixture, wherein a photoelectric cell of the light fixture is coupled to an upper surface of the device housing;
a microphone inside or mounted to the device housing; and
a processor inside the device housing and electrically coupled to the microphone, the processor configured to:
receive a set of audio data from the microphone;
execute a first machine learning model using a segment of the set of audio data comprising a first plurality of sounds as input to determine whether the segment of the set of audio data is likely to contain a sound of a gunshot; and
responsive to determining the segment of the set of audio data is likely to contain the sound of the gunshot based on the execution of the first machine learning model, transmit the segment of the set of audio data to a remote computing device; and
the remote computing device comprising:
a remote processor configured to:
receive the segment of audio data from the computing device; and
execute a second machine learning model using the segment of audio data received from the computing device to output a first timestamp of the gunshot for the computing device;
responsive to executing the second machine learning model using the segment of audio data received from the computing device to output the first timestamp of the gunshot for the computing device, query at least one computing device within a distance threshold of the computing device with the first timestamp of the gunshot for the computing device output by the second machine learning model;
wherein each of the at least one computing device is configured to:
in response to receiving the query containing the first timestamp of the gunshot for the computing device, execute a third machine learning model using a second segment of audio data corresponding to the first timestamp of the gunshot for the computing device to determine whether the second segment of audio data is likely to contain the sound of the gunshot; and
responsive to determining the second segment of audio data is likely to contain the sound of the gunshot based on the execution of the third machine learning model, transmit the second segment of the set of audio data to the remote computing device;
wherein the processor of the remote computing device is configured to:
receive the second segment of audio data from each of the at least one computing device; and
iteratively execute, for each received second segment of audio data, a second machine learning model using the second segment of audio data to output a second timestamp of the gunshot for the second segment of audio data that is configured with the first timestamp for input into a multilateration model to determine a location of the gunshot.
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