Audio Event Detection Using Synthetic Data and Neural Networks
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Solution Overview
Problem
Current audio event detection systems face challenges in effectively detecting and localizing target audio events in environments with limited annotated training data, particularly in scenarios where visual cues are ambiguous, and they require robustness across varying conditions.
Innovation Solution
A method and system utilizing a processor to receive audio clips, determine audio features, and employ a neural network-based approach involving a dilated convolutional neural network for tagging and a bi-directional long short-term memory classifier to detect and localize target audio events, leveraging synthetic datasets to enhance robustness with limited labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional audio event detection systems are used, then they can detect audio events, but they require large amounts of annotated training data which are not available
Solution Approach 1:
The patent uses synthetic data generation to create artificial audio event samples that replicate real-world scenarios. By synthesizing training data through computational models rather than relying on manually annotated real data, the system achieves effective training with minimal actual annotated samples.
Solution Approach 2:
The system performs preliminary data synthesis and augmentation before the actual detection task. By pre-generating synthetic training data and pre-training models on this synthetic data, the system prepares in advance to handle real audio events with limited annotated examples.
2Reliability
If visual surveillance systems are used, then they can detect events with visual characteristics, but they fail in low visibility conditions such as night or fog
Solution Approach 1:
The patent replaces visual detection mechanisms with audio-based detection. Instead of relying on optical sensors and visual processing that fail in low-light conditions, the system uses microphones and audio processing to detect events through sound waves, which are unaffected by illumination conditions.
Solution Approach 2:
The system changes the detection parameter from visual characteristics to acoustic characteristics. By monitoring audio features such as frequency, amplitude, and temporal patterns instead of visual features, the system maintains detection capability across all lighting conditions.
3Reliability
If thermal infrared sensors are used, then they can provide alternative surveillance, but they are highly dependent on temperature and have background separation problems
Solution Approach 1:
The patent replaces thermal infrared sensing with audio sensing. Instead of detecting thermal radiation that is affected by temperature gradients and background separation issues, the system detects acoustic waves that provide distinct signatures for different events without temperature dependence.
4Reliability
If audio event detection is implemented, then it can provide robust surveillance, but it requires sophisticated neural network processing that increases system complexity
Solution Approach 1:
The patent segments the audio event detection process into distinct modular components: feature extraction module, synthetic data generation module, model training module, and event classification module. This segmentation allows each component to be optimized independently and simplifies the overall system architecture despite the sophisticated processing required.
Data Source
AI summary
A method and system for detecting and localizing a target audio event in an audio clip is disclosed. The method and system use utilizes a hierarchical approach in which a dilated convolutional neural network to detect the presence of the target audio event anywhere in an audio clip based on high level audio features. If the target audio event is detected somewhere in the audio clip, the method and system further utilizes a robust audio vector representation that encodes the inherent state of the audio as well as a learned relationship between state of the audio and the particular target audio event that was detected in the audio clip. A bi-directional long short term memory classifier is used to model long term dependencies and determine the boundaries in time of the target audio event within the audio clip based on the audio vector representations.