Systems and methods for assessing sound within a vehicle using machine learning techniques

1. A computer-based method for assessing sound within a vehicle to alert a driver of the vehicle to an approaching emergency services vehicle, the method performed using a sound analysis computing device comprising at least one processor in communication with at least one memory device, the method comprising:

analyzing, based upon a pattern recognition machine learning algorithm, sound data associated with an emergency services vehicle siren to facilitate training the pattern recognition machine learning algorithm;

identifying, based upon the pattern recognition machine learning algorithm, a first characteristic associated with the emergency services vehicle siren;

receiving, from a client device disposed within the vehicle, sound data collected within the vehicle;

analyzing the sound data collected within the vehicle;

identifying, based upon the analyzing and in real time, a second characteristic associated with the sound data collected within the vehicle;

receiving, from an emergency dispatch system, an emergency dispatch signal;

determining, in response to receiving the emergency dispatch signal, a reduced matching threshold;

comparing the first characteristic to the second characteristic; and

determining, based upon the comparing, whether the first characteristic substantially matches the second characteristic within the reduced matching threshold to determine whether the emergency services vehicle is approaching the vehicle.

2. The method of claim 1 , further comprising:

analyzing based upon the pattern recognition machine learning algorithm, sound data associated with a plurality of emergency services vehicle sirens;

identifying, based upon the pattern recognition machine learning algorithm, at least one characteristic associated with each of the plurality of emergency services vehicle sirens;

comparing each of the characteristics associated with each of the plurality of emergency services vehicle sirens to the second characteristic associated with the sound data collected within the vehicle; and

determining, based upon the comparing, whether any of the characteristics associated with each of the plurality of emergency services vehicle sirens substantially matches the second characteristic to determine whether the emergency services vehicle is approaching the vehicle.

3. The method of claim 1 , further comprising identifying, based upon the pattern recognition machine learning algorithm, a first sound signature associated with the emergency services vehicle siren, wherein the first sound signature includes a plurality of characteristics.

4. The method of claim 3 , further comprising identifying, based upon the pattern recognition machine learning algorithm, a second sound signature associated with the sound data collected within the vehicle, wherein the second sound signature includes a plurality of characteristics.

5. The method of claim 4 , further comprising:

comparing the first sound signature to the second sound signature; and

determining, based upon the comparing, whether the first sound signature substantially matches the second sound signature to determine whether the emergency services vehicle is approaching the vehicle.

6. The method of claim 1 , further comprising providing a control instruction to an on-board diagnostics system controller within the vehicle to reduce a volume level associated with at least one entertainment device located within the vehicle.

7. The method of claim 1 , wherein the emergency dispatch signal indicates a location of the emergency services vehicle, the method further comprising analyzing the emergency dispatch signal to determine whether the emergency services vehicle is approaching the vehicle.

8. The method of claim 7 , further comprising generating map directions for display on at least one of the client device and a navigation display of the vehicle based upon the emergency dispatch signal, wherein the map directions identify at least one of the location of the emergency services vehicle or a route to navigate around the emergency services vehicle.

9. The method of claim 7 , further comprising providing an indication to the client device that the emergency services vehicle is approaching the vehicle, such that the client device generates at least one of an audible alert or a visual alert in response.

10. The method of claim 7 , wherein the determining, based upon the comparing, whether the first characteristic substantially matches the second characteristic within the reduced matching threshold to determine whether the emergency services vehicle is approaching the vehicle further comprises:

analyzing the emergency dispatch signal to identify global positioning system (GPS) coordinates of the emergency services vehicle;

determining, based upon the GPS coordinates, whether the emergency services vehicle is approaching the vehicle; and

reducing, in response to determining that the emergency services vehicle is approaching the vehicle, a matching threshold to determine the reduced matching threshold, wherein, in response to reducing the matching threshold, an amount of matching needed to determine that the emergency services vehicle is approaching the vehicle is less than an amount of matching needed to make the same determination in the absence of the emergency dispatch signal.

11. A computer system for assessing sound within a vehicle to alert a driver of the vehicle to an approaching emergency services vehicle, the computer system comprising:

a processor; and

a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising:

analyzing based upon a pattern recognition machine learning algorithm, sound data associated with an emergency services vehicle siren to facilitate training the pattern recognition machine learning algorithm;

identifying, based upon the pattern recognition machine learning algorithm, a first characteristic associated with the emergency services vehicle siren;

receiving, from a client device disposed within the vehicle, sound data collected within the vehicle;

analyzing the sound data collected within the vehicle;

identifying, based upon the analyzing and in real time, a second characteristic associated with the sound data collected within the vehicle;

receiving, from an emergency dispatch system, an emergency dispatch signal;

determining, in response to receiving the emergency dispatch signal, a reduced matching threshold;

comparing the first characteristic to the second characteristic;

determining, based upon the comparing, whether the first characteristic substantially matches the second characteristic within the reduced matching threshold to determine whether the emergency services vehicle is approaching the vehicle.

12. The computer system of claim 11 , wherein the processor is further configured to perform operations comprising:

analyzing, based upon the pattern recognition machine learning algorithm, sound data associated with a plurality of emergency services vehicle sirens;

identifying, based upon the pattern recognition machine learning algorithm, at least one characteristic associated with each of the plurality of emergency services vehicle sirens;

comparing each of the characteristics associated with each of the plurality of emergency services vehicle sirens to the second characteristic associated with the sound data collected within the vehicle; and

determining, based upon the comparing, whether any of the characteristics associated with each of the plurality of emergency services vehicle sirens substantially matches the second characteristic to determine whether the emergency services vehicle is approaching the vehicle.

13. The computer system of claim 11 , wherein the processor is further configured to perform operations comprising identifying, based upon the pattern recognition machine learning algorithm, a first sound signature associated with the emergency services vehicle siren, wherein the first sound signature includes a plurality of characteristics.

14. The computer system of claim 13 , wherein the processor is further configured to perform operations comprising identifying, based upon the pattern recognition machine learning algorithm, a second sound signature associated with the sound data collected within the vehicle, wherein the second sound signature includes a plurality of characteristics.

15. The computer system of claim 14 , wherein the processor is further configured to perform operations comprising:

comparing the first sound signature to the second sound signature; and

determining, based upon the comparing, whether the first sound signature substantially matches the second sound signature to determine whether the emergency services vehicle is approaching the vehicle.

16. The computer system of claim 11 , wherein the processor is further configured to perform operations comprising providing an indication to the client device that the emergency services vehicle is approaching the vehicle, such that the client device generates at least one of an audible alert or a visual alert in response.

17. The computer system of claim 11 , wherein the processor is further configured to perform operations comprising providing a control instruction to an on-board diagnostics system controller within the vehicle to reduce a volume level associated with at least one entertainment device located within the vehicle.

18. The computer system of claim 11 , wherein the processor is further configured to perform operations comprising:

analyzing the emergency dispatch signal to determine whether the emergency services vehicle is approaching the vehicle.

19. The computer system of claim 18 , wherein the processor is further configured to perform operations comprising generating map directions for display on at least one of the client device and a navigation display of the vehicle based upon the emergency dispatch signal, wherein the map directions identify at least one of the location of the emergency services vehicle or a route to navigate around the emergency services vehicle.

20. The computer system of claim 18 , wherein the processor is further configured to perform operations comprising providing an indication to the client device that the emergency services vehicle is approaching the vehicle, such that the client device generates at least one of an audible alert or a visual alert in response.

21. The computer system of claim 18 , wherein the determining, based upon the comparing, whether the first characteristic substantially matches the second characteristic within the reduced matching threshold to determine whether the emergency services vehicle is approaching the vehicle further comprises:

analyzing the emergency dispatch signal to identify global positioning system (GPS) coordinates of the emergency services vehicle;

determining, based upon the GPS coordinates, whether the emergency services vehicle is approaching the vehicle; and

reducing, in response to determining that the emergency services vehicle is approaching the vehicle, a matching threshold to determine the reduced matching threshold, wherein, in response to reducing the matching threshold, an amount of matching needed to determine that the emergency services vehicle is approaching the vehicle is less than an amount of matching needed to make the same determination in the absence of the emergency dispatch signal.

22. A non-transitory computer readable medium that includes executable instructions for assessing sound within a vehicle to alert a driver of the vehicle to an approaching emergency services vehicle, wherein when executed by a sound analysis computing device comprising at least one processor in communication with at least one memory device, the computer executable instructions cause the sound analysis computing device to:

analyze, based upon a pattern recognition machine learning algorithm, sound data associated with an emergency services vehicle siren to facilitate training the pattern recognition machine learning algorithm;

identify, based upon the pattern recognition machine learning algorithm, a first characteristic associated with the emergency services vehicle siren;

receive, from a client device disposed within the vehicle, sound data collected within the vehicle;

analyze the sound data collected within the vehicle;

identify, based upon the analyzing and in real time, a second characteristic associated with the sound data collected within the vehicle;

receive, from an emergency dispatch system, an emergency dispatch signal;

determine, in response to receiving the emergency dispatch signal, a reduced matching threshold;

compare the first characteristic to the second characteristic; and

determine, based upon the comparing, whether the first characteristic substantially matches the second characteristic within the reduced matching threshold to determine whether the emergency services vehicle is approaching the vehicle.

23. The non-transitory computer readable medium of claim 22 , wherein the computer executable instructions further cause the sound analysis computing device to:

analyze, based upon the pattern recognition machine learning algorithm, sound data associated with a plurality of emergency services vehicle sirens;

identify, based upon the pattern recognition machine learning algorithm, at least one characteristic associated with each of the plurality of emergency services vehicle sirens;

compare each of the characteristics associated with each of the plurality of emergency services vehicle sirens to the second characteristic associated with the sound data collected within the vehicle; and

determine, based upon the comparing, whether any of the characteristics associated with each of the plurality of emergency services vehicle sirens substantially matches the second characteristic to determine whether the emergency services vehicle is approaching the vehicle.

24. The non-transitory computer readable medium of claim 22 , wherein the computer executable instructions further cause the sound analysis computing device to identify, based upon the pattern recognition machine learning algorithm, a first sound signature associated with the emergency services vehicle siren, wherein the first sound signature includes a plurality of characteristics.

25. The non-transitory computer readable medium of claim 24 , wherein the computer executable instructions further cause the sound analysis computing device to identify, based upon the pattern recognition machine learning algorithm, a second sound signature associated with the sound data collected within the vehicle, wherein the second sound signature includes a plurality of characteristics.

26. The non-transitory computer readable medium of claim 25 , wherein the computer executable instructions further cause the sound analysis computing device to:

compare the first sound signature to the second sound signature; and

determine, based upon the comparing, whether the first sound signature substantially matches the second sound signature to determine whether the emergency services vehicle is approaching the vehicle.

27. The non-transitory computer readable medium of claim 22 , wherein the computer executable instructions further cause the sound analysis computing device to provide an indication to the client device that the emergency services vehicle is approaching the vehicle, such that the client device generates at least one of an audible alert or a visual alert in response.

28. The non-transitory computer readable medium of claim 22 , wherein the computer executable instructions further cause the sound analysis computing device to provide a control instruction to an on-board diagnostics system controller within the vehicle to reduce a volume level associated with at least one entertainment device located within the vehicle.

29. The non-transitory computer readable medium of claim 22 , wherein the computer executable instructions further cause the sound analysis computing device to

analyze the emergency dispatch signal to determine whether the emergency services vehicle is approaching the vehicle.

30. The non-transitory computer readable medium of claim 29 , wherein the computer executable instructions further cause the sound analysis computing device to generate map directions for display on at least one of the client device and a navigation display of the vehicle based upon the emergency dispatch signal, wherein the map directions identify at least one of the location of the emergency services vehicle or a route to navigate around the emergency services vehicle.

31. The non-transitory computer readable medium of claim 29 , wherein the computer executable instructions further cause the sound analysis computing device to provide an indication to the client device that the emergency services vehicle is approaching the vehicle, such that the client device generates at least one of an audible alert or a visual alert in response.

32. The non-transitory computer readable medium of claim 29 , wherein the computer executable instructions further cause the sound analysis computing device to:

analyze the emergency dispatch signal to identify global positioning system (GPS) coordinates of the emergency services vehicle;

determine, based upon the GPS coordinates, whether the emergency services vehicle is approaching the vehicle; and

reduce, in response to determining that the emergency services vehicle is approaching the vehicle, a matching threshold to determine the reduced matching threshold, wherein, in response to reducing the matching threshold, an amount of matching needed to determine that the emergency services vehicle is approaching the vehicle is less than an amount of matching needed to make the same determination in the absence of the emergency dispatch signal.

33. The method of claim 1 , further comprising

receiving, from the client device disposed within the vehicle, a location of the vehicle by receiving GPS data from the client device;

receiving a location of the emergency services vehicle; and

analyzing the vehicle location, the emergency services vehicle location, the first characteristic, and the second characteristic to determine whether the emergency services vehicle is approaching the vehicle.