17689299. METHOD AND APPARATUS WITH ABNORMAL CHANNEL OF MICROPHONE ARRAY DETECTION AND COMPENSATION SIGNAL GENERATION simplified abstract (Samsung Electronics Co., Ltd.)
METHOD AND APPARATUS WITH ABNORMAL CHANNEL OF MICROPHONE ARRAY DETECTION AND COMPENSATION SIGNAL GENERATION
Organization Name
Inventor(s)
METHOD AND APPARATUS WITH ABNORMAL CHANNEL OF MICROPHONE ARRAY DETECTION AND COMPENSATION SIGNAL GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17689299 titled 'METHOD AND APPARATUS WITH ABNORMAL CHANNEL OF MICROPHONE ARRAY DETECTION AND COMPENSATION SIGNAL GENERATION
Simplified Explanation
The patent application describes a method for detecting abnormal channels in a microphone array using a neural network model. Here is a simplified explanation of the abstract:
- The method involves receiving sound signals from multiple channels of a microphone array.
- The sound signals are synchronized based on the spatial information of the microphone array.
- The synchronized sound signals and conditional information are inputted into a neural network model.
- The neural network model performs an inverse operation to detect any abnormal channels in the microphone array.
Potential Applications:
- Audio recording and processing systems
- Speech recognition systems
- Acoustic monitoring systems
Problems Solved:
- Identifying abnormal channels in a microphone array can be challenging.
- Manual inspection of each channel is time-consuming and prone to errors.
- Existing methods may not effectively detect abnormal channels.
Benefits:
- The method provides an automated and efficient way to detect abnormal channels.
- It improves the accuracy and reliability of sound source localization.
- It reduces the need for manual inspection and intervention.
Original Abstract Submitted
A method includes: receiving multi-channel sound source signals from a microphone array; synchronizing the multi-channel sound source signals based on spatial information of the microphone array; and detecting an abnormal channel of the microphone array by inputting the synchronized sound source signals and first conditional information to a neural network model configured to perform an inverse operation.
