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
- Simplified Explanation:**
The patent application describes an acoustic environment identification system that utilizes neural networks to accurately identify different environments based on audio data.
- Key Features and Innovation:**
- Uses convolutional neural networks to generate audio feature data.
- Utilizes a recursive neural network to process the audio feature data and generate characterization data.
- Implements a weighting system to modify the characterization data by weighting signature data items.
- Employs classification neural networks to classify the identified environments.
- Potential Applications:**
This technology can be used in various applications such as:
- Smart home systems for automatically adjusting settings based on the identified environment.
- Security systems for detecting unauthorized access based on environmental sounds.
- Industrial applications for monitoring and optimizing acoustic environments in factories or warehouses.
- Problems Solved:**
- Accurately identifying different acoustic environments.
- Automating processes based on environmental cues.
- Enhancing security measures through audio-based detection.
- Benefits:**
- Improved efficiency in environmental identification.
- Enhanced automation capabilities.
- Increased security measures through audio analysis.
- Commercial Applications:**
Title: Acoustic Environment Identification System for Smart Homes and Security Systems This technology has potential commercial applications in smart home systems, security systems, and industrial settings. It can revolutionize how environments are monitored and controlled based on audio cues, leading to increased efficiency and security measures.
- Questions about Acoustic Environment Identification Systems:**
1. How does the weighting system in the characterization data modification process work? 2. What are the specific neural networks used in the acoustic environment identification system and how do they contribute to accurate identification?
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.
