Audio Data Augmentation via Non-Target Segment Replacement
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Solution Overview
Problem
Current machine learning model training for sound event detection (SED) systems is hindered by the laborious, costly, and time-consuming process of data collection and labeling, leading to overfitting and poor generalization, especially with limited diverse training samples. Additionally, interference from non-target audio events degrades model performance.
Innovation Solution
The method involves receiving first and second audio data, identifying non-target background audio segments using labels, and generating augmented training data sets by replacing these segments between the two datasets. This approach enhances model robustness by increasing sample variability without altering original labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection and labeling are performed manually, then training data quality is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates synthetic copies of audio data by generating augmented training data sets that replicate real audio characteristics. These synthetic copies are then used to train models, eliminating the need for manual labeling of every training sample while maintaining data quality through systematic generation processes.
Solution Approach 2:
The system performs self-labeling by automatically identifying and labeling audio segments without human intervention. The patent implements automated processes that analyze audio data, identify relevant segments, and create training data sets independently, significantly reducing the time and resources required for manual annotation.
2Adaptability or versatility
If training data diversity is increased, then model generalization is improved, but data collection complexity increases
Solution Approach 1:
The patent systematically varies audio parameters such as background noise levels, acoustic environments, and temporal characteristics to generate diverse training data sets. By manipulating these parameters programmatically, the system creates varied training scenarios without requiring complex field collection operations, thereby improving model generalization while maintaining collection simplicity.
Solution Approach 2:
The system divides audio data into discrete segments and creates multiple variations of these segments through augmentation techniques. This segmentation allows for systematic generation of diverse training data by combining and modifying individual audio clips, reducing the complexity of collecting and managing large-scale diverse data sets.
3Reliability
If non-target background audio segments are present in training data, then model robustness is improved, but training data purity decreases
Solution Approach 1:
The patent converts the presence of non-target background audio, which could be considered harmful or unwanted, into a beneficial training feature. By deliberately including and labeling these background segments in controlled ways, the system teaches models to distinguish between target and non-target sounds, improving robustness while maintaining data quality through intentional design rather than accidental contamination.
Data Source
AI summary
A method for training at least one machine learning model includes receiving first audio data, receiving second audio data, and identifying, using labels associated with segments of the first audio data, non-target background audio segments in the first audio data. The method also includes identifying, using labels associated with segments of the second audio data, non-target background audio segments in the second audio data, generating a first augmented training data set by replacing the identified non-target background audio segments in the first audio data with the identified non-target background audio segments in the second audio data, generating a second augmented training data set by replacing the identified non-target background audio segments in the second audio data with the identified non-target background audio segments in the first audio data, and training at least one machine learning model using the first augmented training data set and the second augmented training data set.