Crowdsourced Sound Identification Database
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Solution Overview
Problem
Conventional audio systems struggle to accurately detect specific types of sounds in ambient environments, as they are typically preprogrammed to recognize only generic sounds and cannot effectively identify unique sound sources, such as specific vehicles or power tools, due to hardware and processing limitations.
Innovation Solution
A method and system that crowdsources audio recordings from multiple devices to generate large datasets, processing these recordings to determine specific sound parameters, allowing for accurate identification of unique sound types without requiring significant hardware or processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems are preprogrammed to recognize generic sounds, then the system can detect general ambient sounds, but it cannot accurately identify specific types of sounds such as specific vehicles or power tools
Solution Approach 1:
The system pre-collects and stores sound samples from multiple sources in a database before actual use. When a user needs to identify a specific sound, the system retrieves pre-processed sound parameters from the database rather than analyzing raw audio in real-time, enabling both high precision and broad coverage of sound types
Solution Approach 2:
The system creates simplified representations (sound parameters) of actual sound recordings. These parameter sets serve as copies that capture the essential characteristics of specific sounds without requiring storage of complete audio files, enabling efficient comparison and identification
2Measurement precision
If the system collects and processes large datasets to improve sound identification accuracy, then specific sound types can be identified, but hardware and processing costs increase significantly
Solution Approach 1:
The system divides the sound analysis task into two segments: offline data collection and processing (performed once to build the database) and online query and matching (performed rapidly when identification is needed). This segmentation allows heavy processing to be done in advance, reducing real-time power requirements
Solution Approach 2:
Sound parameters are extracted and stored in the database in advance during data collection phases. When identification is needed, the system only performs lightweight parameter matching rather than full audio analysis, dramatically reducing processing power requirements at the moment of use
Data Source
AI summary
One embodiment of the present invention sets forth a technique for determining a set of sound parameters associated with a sound type. The technique includes receiving, via a network and from each a first plurality of remote computing devices, an audio recording of a first sound type and a descriptor associated with the first sound type. The technique further includes processing the audio recordings via a processor to determine a first set of sound parameters associated with the first sound type. The technique further includes receiving a request associated with the descriptor from at least one remote computing device and, in response, transmitting the first set of sound parameters associated with the first sound type to the at least one remote computing device.