Fine-grained in-vehicle dynamic noise pattern learning for voice applications

A vehicle for in-cabin noise-pattern learning for voice applications, comprising:

• a vehicle body including a cabin arranged therein;
• a memory;
• a processor coupled to the memory and configured to:
• embed multimodal data from environment and vehicle data;
• embed acoustic data from microphone-captured data in the cabin;
• concatenate the embeddings to form a latent vector characterizing the embeddings;
• estimate a mean and variance of the latent vector via an adaptive time window;
• and
• identify a noise type using the mean and variance of the latent vector, wherein the noise type identification is fine-grained to accurately emulate vehicle noise.

A system for in-vehicle noise pattern learning for voice applications, comprising:

• a vehicle body including a cabin arranged therein;
• a memory;
• a processor coupled to the memory, the processor and memory being coupled within the vehicle body, the processor being configured to:
• embed multimodal data from environment and vehicle data;
• embed acoustic data from microphone-captured data in the cabin;
• concatenate the embeddings to form a latent vector characterizing the embeddings;
• estimate a mean and variance of the latent vector via an adaptive time window;
• and
• identify a noise type using the mean and variance of the latent vector, wherein the noise type identification is fine-grained to accurately emulate vehicle noise.