Indoor Scene Recognition via Ambient Sound Analysis
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Solution Overview
Problem
Current location technologies, such as GPS, WIFI, and GSM, are ineffective for indoor scene recognition due to varying signal strengths and infrastructure costs, leading to inaccurate location determination in indoor environments.
Innovation Solution
A scene recognition method and device utilizing ambient sound, comprising a sound collection module, preprocessing module, feature extraction module, and scene recognition module, which processes sound signals into frequency domain signals, extracts feature information, and matches it with stored weight values to determine the scene, allowing for accurate indoor location recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If antenna-based locating technologies (GPS, WIFI, GSM) are used for indoor locating, then outdoor locating accuracy is improved, but indoor locating accuracy deteriorates due to signal fluctuations and electromagnetic interference
Solution Approach 1:
The patent replaces antenna-based electromagnetic signal locating systems with an acoustic field-based sound recognition system. Instead of relying on GPS, WIFI, or GSM signals that suffer from electromagnetic interference and signal fluctuations indoors, the system uses sound signal collection and analysis to determine indoor location and scene recognition, fundamentally substituting the physical basis of the locating mechanism.
Solution Approach 2:
The patent introduces sound signals as an intermediary medium for indoor locating. Sound waves serve as a mediator that can penetrate building structures and reflect off surfaces, providing stable information about indoor spatial configuration. The system collects sound signals from multiple directions and uses acoustic signal processing to infer location, avoiding direct electromagnetic interference issues.
2Area of stationary object
If WIFI signals are used for indoor locating, then signal coverage is improved, but locating accuracy deteriorates due to great signal fluctuations and uneven variations
Solution Approach 1:
The patent changes the physical parameter used for locating from electromagnetic signal strength (WIFI) to acoustic signal characteristics (sound waves). By analyzing parameters such as sound intensity, frequency spectrum, and temporal patterns of sound signals, the system achieves locating accuracy that is not affected by the fluctuating nature of WIFI signal strength, while maintaining comprehensive indoor coverage.
3Reliability
If base station signals are used for locating, then signal stability is improved, but locating capability deteriorates due to sparse deployment and high infrastructure requirements
Solution Approach 1:
The patent enables the mobile terminal to perform self-localization using its own sound collection capabilities. Instead of relying on external base station infrastructure, the terminal uses its microphone array to collect sound signals, processes them through acoustic signal analysis, and determines its own location and surrounding scene. This self-service approach eliminates dependency on sparse base station deployment while maintaining signal stability through acoustic wave propagation characteristics.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enables mobile terminals to quickly and accurately recognize indoor scenes while maintaining low consumption, complementing antenna-based locating methods and improving overall performance.
Implementation Method 1
the preprocessing module is configured to process the sound signal into a frequency domain signal
Data Source
AI summary
The present document provides a scene recognition method and device based on ambient sound and a mobile terminal. The device includes: a sound collection module, a preprocessing module, a feature extraction module, a scene recognition module and a database. The method includes: collecting a sound signal; processing the sound signal into a frequency domain signal; extracting sound feature information from the frequency domain signal; inputting the sound feature information under a preset model, matching a model output result with weight values of sound sample models of scenes, and determining a scene corresponding to the sound feature information. The present document implements locating based on background sound information as the feature of the scene, so that the mobile terminal quickly and correctly recognizes the current scene under the maintaining low-consumption state.