Scene Identification Using Chronological Sound Sequences
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Solution Overview
Problem
Current sound recognition systems are inadequate for identifying complex situations or scenes based on multiple sounds due to insufficiently provisioned and varied sound databases, leading to unreliable and erroneous identifications, as they primarily focus on single sound identification and lack effective search or selection systems.
Innovation Solution
A device that identifies scenes by capturing and classifying multiple sounds in chronological order, using complementary data from connected devices to refine sound classes and distinguish between similar acoustic fingerprints, thereby improving scene interpretation and reducing uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current sound recognition systems use single sound identification, then the system complexity is reduced, but the reliability of scene identification deteriorates
Solution Approach 1:
The patent segments the sound identification process into multiple stages: individual sound classification, temporal ordering, and scene-level synthesis. By analyzing sounds in discrete temporal segments and combining them through ordering relationships, the system achieves reliable scene identification without requiring a monolithic complex system.
Solution Approach 2:
The patent introduces a temporal ordering dimension to sound analysis. Instead of merely classifying individual sounds, the system orders them chronologically and uses this temporal dimension to distinguish between different scenes that may produce similar sound sequences, thereby improving reliability without proportionally increasing complexity.
2Device complexity
If sound databases are insufficiently provisioned and varied, then the device complexity is reduced, but the measurement precision of sound classification deteriorates
Solution Approach 1:
The system performs self-service by automatically collecting, organizing, and annotating sound samples from the environment. It builds its own database through continuous operation, capturing sounds during various scenes and using these real-world examples to train and refine its classification models, thereby improving precision without requiring externally provisioned comprehensive databases.
Solution Approach 2:
The patent implements feedback mechanisms where the system uses identified scenes and their associated sounds to continuously improve its classification accuracy. By comparing detected sounds with actual scene contexts and using this feedback to refine future identifications, the system progressively enhances measurement precision.
3Ease of operation
If current sound identification systems use simple sound classes, then the ease of operation is improved, but the loss of information about sound variability increases
Solution Approach 1:
The patent introduces dynamic adaptability to sound classification by allowing the system to automatically adjust the granularity and specificity of sound classes based on the context and available information. The classification system evolves from simple to more detailed levels as needed, maintaining ease of operation while capturing sound variability through contextual enrichment.
Data Source
AI summary
An identification device, method and system for identifying a scene in an environment. The environment includes at least one sound capture device. The identification device is configured to identify the scene based on at least two sounds captured in the environment. Each of the at least two sounds are associated respectively with at least one sound class. The scene is identified by taking account of a chronological order in which the at least two sounds were captured.