Method and system for sound monitoring over a network
A mobile communication environment ( 100 ) can include a mobile device ( 160 ) to measure and send sound pressure level data. The mobile device ( 160 ) can initiate the collection of audio information responsive to detecting a trigger event. Mobile device ( 160 ) can measure or calculate the sound pressure level from the audio information. Metadata including time information and geographic location information can be captured with the collected audio information. Mobile device ( 160 ) can send the sound pressure level data and metadata through a wired or wireless communication path to a database ( 614 ).
1. A wearable device, comprising:
a microphone;
a memory that stores instructions; and
a processor that executes the instructions to perform operations, the operations comprising:
receiving an acoustic signal from the microphone configured to measure an ambient environment;
analyzing the acoustic signal to detect a trigger event;
opening a communication channel with a remote server if a trigger event is detected;
generating metadata;
transmitting the metadata, and the acoustic signal to the server via the communication channel; and
receiving, from the server an analysis of the acoustic signal including whether a sound signature has been detected.
2. The device of claim 1 , wherein the operations further comprise transmitting the metadata and the acoustic signal as a package to the server over the communication channel.
3. The device of claim 1 , wherein the operations further comprise transmitting the metadata, and the acoustic signal separately to the server via separate communications over the communication channel.
4. The device of claim 1 , wherein the operations further comprise monitoring the acoustic signal and a plurality of other acoustic signals occurring in the environment.
5. The device of claim 1 , wherein the operations further comprise buffering the acoustic signal upon detection of the trigger event.
6. The device of claim 1 , wherein the operations further comprise presenting details of the analysis to a user in a visual format, an audible format, or a combination thereof.
7. The device of claim 1 wherein the operations further comprise receiving a message indicating that a sound pressure level associated with the acoustic signal is harmful if sustained over a period of time.
8. The device of claim 1 , wherein the operations further comprise presenting a threat level associated with the sound signature.
9. The device of claim 8 , wherein the operations further comprise providing an instruction for responding based on the threat level associated with the sound signature.
10. The device of claim 1 where a trigger for the trigger event is at least one of a sound pressure level, an acceleration value, a velocity value, and chemical detection.
11. The device of claim 1 , wherein the operations further comprise receiving an identification of the sound signature from the server.
12. A method, comprising:
receiving an acoustic signal from a microphone of a wearable device and the microphone is configured to measure an ambient environment;
analyzing the acoustic signal to detect a trigger event;
opening a communication channel with a remote server if the trigger event is detected;
extracting a feature from the acoustic signal;
generating metadata;
transmitting the metadata and the acoustic signal to the server via the communication channel; and
receiving, from the server an analysis of the acoustic signal, including whether a sound signature has been detected.
13. The method of claim 12 , further comprising detecting the trigger event upon detection of a change in sound pressure level in the environment.
14. The method of claim 12 , further comprising parsing a tag associated with the acoustic signal to determine if the acoustic signal contains a previously learned sound signature.
15. The method of claim 12 , further comprising updating a sound signature model associated with the sound signature based on the acoustic signal.
16. The method of claim 12 , further comprising training a new model for the sound signature to learn a characteristic of the acoustic signal.