Electronic Apparatus Noise Clustering for Adaptive ANC
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Solution Overview
Problem
Existing audio devices struggle to effectively cancel noise in dynamic environments where noise profiles are not pre-stored, leading to suboptimal noise cancellation performance.
Innovation Solution
An electronic apparatus that classifies audio data into clusters based on features and metadata, identifies matching noise clusters, updates a local database with new noise profiles, and generates anti-noise data using external server information when needed, optimizing noise cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If active noise cancellation uses pre-stored noise profiles, then noise cancellation performance is optimized for known environments, but noise cancellation effectiveness deteriorates in dynamic or unfamiliar environments
Solution Approach 1:
The system transitions from static pre-stored noise profiles to dynamic real-time noise classification. The processor continuously classifies noise into clusters based on current audio data and metadata, adapting to changing environments rather than relying on fixed profiles
Solution Approach 2:
The system performs self-learning by automatically classifying noise clusters and updating the local database without requiring manual intervention. The electronic apparatus autonomously adapts to new environments by identifying and storing new noise cluster information
2Adaptability or versatility
If the local database is continuously updated with new noise clusters, then adaptability to new environments improves, but device complexity and processing requirements increase
Solution Approach 1:
The noise classification system divides complex noise environments into discrete clusters based on similarity metrics. Each noise cluster represents a categorized noise type, simplifying the processing of continuous audio data into manageable discrete units
Solution Approach 2:
The system performs noise cluster classification and database updates only when necessary - specifically when new noise patterns are detected that don't match existing clusters. This selective updating reduces unnecessary processing while maintaining adaptability
3Speed
If noise classification is performed in real-time, then responsiveness to changing noise environments improves, but energy consumption increases
Solution Approach 1:
The system performs noise cluster classification periodically or event-driven rather than continuously. Classification is triggered by changes in noise patterns or metadata, reducing computational load while maintaining responsiveness to significant environmental changes
Solution Approach 2:
The system pre-processes audio data into noise clusters and stores them locally for quick retrieval. This preliminary classification reduces the computational burden during real-time operation, as the system only needs to match current noise against existing clusters rather than performing full analysis
Data Source
AI summary
A control method of an electronic apparatus for generating anti-noise for acoustic noise cancellation (ANC) is provided. The method includes obtaining audio data and metadata of the audio data, classifying the audio data into a plurality of audio clusters based on at least one of a feature of the audio data or a feature of the metadata, identifying whether each audio cluster of the plurality of audio clusters is included in a local database corresponding to the location information of a place where the audio data is obtained, based on the at least one of the plurality of the audio clusters being not included in the local database, updating the local database by adding information regarding the audio cluster that is not included in the plurality of noise clusters to information regarding the plurality of noise clusters and generating anti-noise data based on the information regarding the local database which is updated.