Audio Recognition Feature Fusion for Semantic and Position Accuracy
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Solution Overview
Problem
Existing deep learning-based audio recognition techniques often sacrifice accurate position information in feature maps for higher-level semantic information, leading to incomplete feature extraction.
Innovation Solution
The method involves obtaining a target feature map by reconstructing multi-level feature maps to incorporate both rich semantic and high-resolution position information, and augmenting the feature representation using data augmentation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If convolution operations are used for feature extraction in deep learning-based audio recognition, then rich high-level semantic information is obtained, but accurate position information is lost
Solution Approach 1:
The patent segments the feature extraction process into multiple levels, where different convolutional layers extract features at different levels of abstraction. Early layers preserve position information while later layers extract semantic information, and the patent combines these segmented features through feature fusion to maintain both position and semantic information simultaneously.
Solution Approach 2:
The patent introduces a new dimension by adding position encoding to the frequency-domain audio features. This position encoding dimension preserves spatial/temporal position information that would otherwise be lost in the frequency domain, allowing the model to maintain both position and semantic information through multi-dimensional feature representation.
2Measurement precision
If multi-level feature maps are reconstructed to include both semantic and position information, then feature extraction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating position encoding matrices and storing them for reuse during inference. This avoids redundant computation of position information at each processing step, reducing computational complexity while maintaining the accuracy benefits of multi-level feature reconstruction.
Solution Approach 2:
The patent uses copying by replicating position encoding information across multiple feature map levels and using data augmentation to create additional training samples. This allows the model to learn from diverse examples without requiring proportionally increased computational resources for each new sample.
Data Source
AI summary
Embodiments of the present disclosure provide an audio recognition method and apparatus, an electronic device, and a computer program product. The method may include obtaining a target feature map of audio data based on a multi-level feature map of the audio data. The method may further include determining a feature representation of the audio data based on the target feature map. In addition, the method may further include determining a recognition result for the audio data at least based on the feature representation. By means of implementing the technical solution of the present disclosure, a determined feature representation has high-resolution position information, thereby optimizing the model performance and improving the user experience.