View source for Synaptics Incorporated (20240242726). MULTI-PASS NEURAL NETWORK FOR SPEECH ENHANCEMENT simplified abstract

=MULTI-PASS NEURAL NETWORK FOR SPEECH ENHANCEMENT=


==Organization Name==

[[:Category:Synaptics Incorporated|Synaptics Incorporated]]


[[Category:Synaptics Incorporated]]

==Inventor(s)==

[[:Category:Saeed Mosayyebpour Kaskari of Irvine CA (US)|Saeed Mosayyebpour Kaskari of Irvine CA (US)]][[Category:Saeed Mosayyebpour Kaskari of Irvine CA (US)]]

==MULTI-PASS NEURAL NETWORK FOR SPEECH ENHANCEMENT - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240242726 titled 'MULTI-PASS NEURAL NETWORK FOR SPEECH ENHANCEMENT

The patent application describes methods, devices, and systems for audio signal processing, specifically focusing on multi-pass neural networks for speech enhancement. 

* Deep Neural Network (DNN) and Statistical Signal Processor (SSP) work together in a speech enhancement system.
* DNN receives an input audio signal and infers a speech signal based on a neural network model.
* SSP further denoises the speech signal output by the DNN using statistical signal processing operations.
* Denoised speech signal can be fed back into the DNN for further speech enhancement through a feedback loop.
* The system recursively filters or suppresses residual noise in the speech signal over multiple passes or iterations.

Potential Applications:
- Speech enhancement in noisy environments
- Audio processing in telecommunications
- Improving speech recognition systems

Problems Solved:
- Enhancing speech quality in noisy conditions
- Removing background noise from audio signals

Benefits:
- Improved speech intelligibility
- Enhanced audio quality
- Better performance of speech recognition systems

Commercial Applications:
- Telecommunications industry for clearer audio transmission
- Audio recording and editing software for noise reduction

Questions about the technology:
1. How does the multi-pass neural network approach improve speech enhancement compared to traditional methods?
2. What are the potential limitations of using deep neural networks for speech enhancement?

Frequently Updated Research:
- Stay updated on advancements in deep learning techniques for audio signal processing.


==Original Abstract Submitted==

this disclosure provides methods, devices, and systems for audio signal processing. the present implementations more specifically relate to multi-pass neural networks configured for speech enhancement. in some aspects, a speech enhancement system may include a deep neural network (dnn) and a statistical signal processor (ssp). the dnn is configured to receive an input audio signal and infer a speech signal representing a speech component of the input audio signal based on a neural network model. the ssp is configured to further denoise the speech signal output by the dnn based on one or more statistical signal processing operations. in some implementations, the denoised speech signal may be fed back into the dnn (as an input audio signal) for further speech enhancement. as such, the speech enhancement system may recursively filter or suppress residual noise in the speech signal over a number of passes or iterations of a feedback loop.



[[Category:G10L21/0208]]
[[Category:G10L25/30]]
[[Category:CPC_G10L21/0208]]