Acoustic Scene Classification via Event Segmentation
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Solution Overview
Problem
Existing acoustic scene classification techniques face challenges in identifying and generalizing sound events in real environments due to the need for manual definition and selection of specific events, which becomes impractical with the large number of events and their variable occurrence across environments.
Innovation Solution
The approach identifies generic event types by partitioning audio signals into short-event, long-event, and background frames based on change measures, allowing for improved feature extraction and classification without requiring specific event detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual definition and selection of specific sound events is performed, then event identification accuracy is improved, but device complexity and ease of operation deteriorate due to the large number of events and variable occurrence across environments
Solution Approach 1:
The system automatically defines and selects sound events by analyzing acoustic patterns in audio recordings without requiring manual intervention. The processor identifies events based on detected acoustic patterns and automatically adds them to the event list, allowing the system to serve itself in the event definition task.
Solution Approach 2:
The system changes the approach from manual event definition to automated pattern-based event detection. By detecting acoustic patterns and deriving events from these patterns, the system transforms the parameter of event identification from human-defined categories to algorithmically-derived events based on acoustic characteristics.
2Measurement precision
If manual definition and selection of specific sound events is performed, then event identification accuracy is improved, but ease of operation worsens due to the impracticality of managing large number of events
Solution Approach 1:
The system automatically defines and selects sound events by analyzing acoustic patterns in audio recordings without requiring manual intervention. The processor identifies events based on detected acoustic patterns and automatically adds them to the event list, allowing the system to serve itself in the event definition task.
3Measurement precision
If specific event detection is implemented, then classification accuracy for known events is improved, but adaptability to new environments and unseen events deteriorates
Solution Approach 1:
The system uses acoustic pattern detection as a universal mechanism that can identify both known and unknown events across different environments. By detecting acoustic patterns rather than relying on pre-defined event categories, the system achieves multi-functionality in event identification that adapts to various acoustic scenes.
Solution Approach 2:
The system changes the approach from manual event definition to automated pattern-based event detection. By detecting acoustic patterns and deriving events from these patterns, the system transforms the parameter of event identification from human-defined categories to algorithmically-derived events based on acoustic characteristics.
Data Source
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AI summary
An apparatus and a method for acoustic scene classification of a block of audio samples. The block is partitioned into frames in the time domain. For each frame of a plurality of frames of the block, a change measure between the frame and a preceding frame of the block is calculated. The frame is assigned to one of a set of short-event frames, a set of long-event frames, and a set of background frames, based on the calculated change measure. The feature vector is determined based on a feature computed from the set of short-event frames, the set of long-event frames, and the set of background frames.