Synaptics Incorporated (20240257822). SPATIO-TEMPORAL BEAMFORMER simplified abstract
Contents
SPATIO-TEMPORAL BEAMFORMER
Organization Name
Inventor(s)
Saeed Mosayyebpour Kaskari of Irvine CA (US)
Alireza Masnadi-shirazi of Irvine CA (US)
SPATIO-TEMPORAL BEAMFORMER - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240257822 titled 'SPATIO-TEMPORAL BEAMFORMER
The patent application describes a method for signal processing, specifically focusing on a spatio-temporal beamformer system.
- The system processes audio signals from multiple microphones, transforming time-domain samples into frequency-domain samples and determining speech probability using a neural network model.
- Based on the probability of speech, the system calculates a minimum variance distortionless response (MVDR) beamforming filter for each microphone.
Potential Applications:
- This technology can be used in noise-canceling headphones, conference call systems, and surveillance equipment.
- It can enhance speech recognition systems in smart devices and improve audio quality in video conferencing applications.
Problems Solved:
- Addresses the challenge of isolating speech signals in noisy environments.
- Improves the accuracy of speech recognition by enhancing the quality of audio input.
Benefits:
- Enhanced audio quality in various applications.
- Improved speech recognition performance in noisy conditions.
- Better user experience in communication devices.
Commercial Applications:
- This technology can be applied in consumer electronics, security systems, and telecommunication devices.
- It has potential uses in automotive audio systems and virtual assistants.
Prior Art:
- Researchers in the field of audio signal processing have explored similar techniques for beamforming and speech enhancement.
- Previous patents may exist for beamforming systems utilizing neural network models for speech probability estimation.
Frequently Updated Research:
- Ongoing research may focus on optimizing the neural network model for more accurate speech probability estimation.
- Researchers may be exploring real-time implementation of the beamforming system for practical applications.
Questions about Spatio-Temporal Beamformer: 1. How does the neural network model improve speech probability estimation in the beamforming system?
- The neural network model analyzes frequency-domain samples to determine the likelihood of speech presence, enhancing the accuracy of beamforming filters.
2. What are the key differences between traditional beamforming systems and spatio-temporal beamformers?
- Spatio-temporal beamformers consider both spatial and temporal aspects of audio signals, allowing for more precise speech isolation in noisy environments.
Original Abstract Submitted
this disclosure provides methods, devices, and systems for signal processing. the present implementations relate more specifically to a spatio-temporal beamformer. in some aspects, a beamforming system may receive an audio signal via a plurality of microphones, the audio signal including a number (b) of frames for each of the plurality of microphones, each of the b frames for each of the plurality of microphones including a number (n) of time-domain samples. for a first microphone, the beamforming system may transform the b*n time-domain samples into b*n/2 first frequency-domain samples; transform the b*n/2 first frequency-domain samples into b*n/2 second frequency-domain samples; and determine a probability of speech associated with the b*n/2 second frequency-domain samples based on a neural network model. the beamformer system may determine a minimum variance distortionless response (mvdr) beamforming filter based at least in part on the probability of speech for the first microphone.
