Deep learning active sound design system and methods

1 . A system for active sound design (ASD) generation, comprising:

one or more speakers; and

a computing device, comprising a processor and a memory, wherein the memory is configured to store instructions that, when executed by the processor, are configured to cause the processor to:

receive one or more inputs for synthetic sound generation;

classify the one or more inputs as fast refresh rate inputs (FRRIs) or slow refresh rate inputs (SRRIs);

assign one or more processing resources as a function of refresh rate, wherein:

lower processing priority is assigned to SRRIs, and

higher processing priority is assigned to FRRIs;

generate ASD based on the one or more inputs, wherein the generating comprises:

using the SSRIs to change one or more weights of a deep learning model to be used with one or more prompts,

wherein the prompts are inputs into the deep learning model;

using the deep learning model, processing the weights and prompts to output one or more looping sound files to a stem library, forming one or more stems;

using the FRRIs to change one or more dynamics of a wave synthesis ASD module; and

generating, using the one or more stems and the wave synthesis ASD module, a synthetic sound; and

play the synthetic sound on the one or more speakers.

2 . The system of claim 1 , wherein the FRRIs comprise one or more inputs selected from the group consisting of:

throttle position;

motor speed;

wheel speed;

brake position;

vehicle g-forces; and

motor load.

3 . The system of claim 1 , wherein the SRRIs comprise one or more inputs selected from the group consisting of:

time of day;

one or more calendar dates;

location;

drive mode;

weather;

traffic conditions;

aggressiveness;

complexity;

musicality;

one or more stored personal model weights;

one or more shared model weights; and

user ASD history.

4 . The system of claim 1 , wherein the deep learning model comprises a diffusion model.

5 . The system of claim 1 , wherein the prompts are defined by how the deep learning model is built and trained.

6 . The system of claim 1 , wherein the synthetic sound is a synthetic powertrain sound.

7 . The system of claim 1 , further comprising a vehicle,

wherein the one or more speakers are coupled to the vehicle.

8 . The system of claim 1 , wherein the one or more stems are manipulated by one or more ASD dynamic curves.

9 . The system of claim 8 , wherein each stem, of the one or more stems, comprises multiple ASD dynamic curves for each FRRI.

10 . The system of claim 1 , wherein the instructions, when executed by the processor, are further configured to cause the processor to enable a first user to share one or more stems of a first stem library with a second user.

11 . A method for active sound design (ASD) generation, comprising:

receiving one or more inputs for synthetic sound generation;

classifying the one or more inputs as fast refresh rate inputs (FRRIs) or slow refresh rate inputs (SRRIs);

assigning one or more processing resources as a function of refresh rate, wherein:

lower processing priority is assigned to SRRIs, and

higher processing priority is assigned to FRRIs; and

using a computing device comprising a processor and a memory, generating ASD based on the one or more inputs, wherein the generating comprises:

using the SSRIs to change one or more weights of a deep learning model to be used with one or more prompts,

wherein the prompts are inputs into the deep learning model;

using the deep learning model, processing the weights and prompts to output one or more looping sound files to a stem library, forming one or more stems;

using the FRRIs to change one or more dynamics of a wave synthesis ASD module; and

generating, using the one or more stems and the wave synthesis ASD module, a synthetic sound; and

playing the synthetic sound on one or more speakers.

12 . The method of claim 11 , wherein the FRRIs comprise one or more inputs selected from the group consisting of:

throttle position;

motor speed;

wheel speed;

brake position;

vehicle g-forces; and

motor load.

13 . The method of claim 11 , wherein the SRRIs comprise one or more inputs selected from the group consisting of:

time of day;

one or more calendar dates;

location;

drive mode;

weather;

traffic conditions;

aggressiveness;

complexity;

musicality;

one or more stored personal model weights;

one or more shared model weights; and

user ASD history.

14 . The method of claim 11 , wherein the deep learning model comprises a diffusion model.

15 . The method of claim 11 , wherein the prompts are defined by how the deep learning model is built and trained.

16 . The method of claim 11 , wherein the synthetic sound is a synthetic powertrain sound.

17 . The method of claim 11 , wherein the one or more speakers are coupled to a vehicle.

18 . The method of claim 11 , further comprising manipulating the one or more stems by one or more ASD dynamic curves.

19 . The method of claim 18 , wherein each stem, of the one or more stems, comprises multiple ASD dynamic curves for each FRRI.

20 . The method of claim 11 , further comprising enabling a first user to share one or more stems of a first stem library with a second user.