Snap Inc. (20240282330). ACOUSTIC NEURAL NETWORK SCENE DETECTION simplified abstract
Contents
ACOUSTIC NEURAL NETWORK SCENE DETECTION
Organization Name
Inventor(s)
Jinxi Guo of Los Angeles CA (US)
Jia Li of Marina Del Rey CA (US)
ACOUSTIC NEURAL NETWORK SCENE DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240282330 titled 'ACOUSTIC NEURAL NETWORK SCENE DETECTION
The patent application describes an acoustic environment identification system that utilizes neural networks to accurately identify different environments based on audio data.
- The system employs one or more convolutional neural networks to extract audio features from the input data.
- A recursive neural network processes the audio feature data to generate characterization data, providing a detailed analysis of the acoustic environment.
- A weighting system is used to modify the characterization data by assigning weights to signature data items, enhancing the accuracy of the identification process.
- Classification neural networks are then utilized to classify the environment based on the generated characterization data.
Potential Applications: - Environmental monitoring and analysis - Security and surveillance systems - Smart home technology for automated adjustments based on ambient noise levels
Problems Solved: - Accurate identification of acoustic environments - Efficient processing of audio data for classification purposes
Benefits: - Enhanced environmental awareness - Improved security measures - Automated systems for personalized user experiences
Commercial Applications: Title: "Advanced Acoustic Environment Identification System for Enhanced Security and Automation" This technology can be applied in security systems for identifying potential threats based on environmental sounds, as well as in smart home devices for creating personalized settings based on the surrounding noise levels.
Questions about the technology: 1. How does the weighting system improve the accuracy of the characterization data?
- The weighting system assigns importance to specific data items, allowing for a more precise analysis of the acoustic environment.
2. What are the key advantages of using neural networks in acoustic environment identification?
- Neural networks can efficiently process large amounts of audio data and extract meaningful features for accurate classification.
Original Abstract Submitted
an acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. the acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. a recursive neural network can process the audio feature data to generate characterization data. the characterization data can be modified using a weighting system that weights signature data items. classification neural networks can be used to generate a classification of an environment.
