Audio Anomaly Detection Using Contrastive Latent Clustering
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Solution Overview
Problem
Gathering a large dataset of acoustic anomalies is difficult and costly, as it often requires intentionally breaking functioning machines or parts to collect anomalous data, which is limiting and unfeasible in video-surveillance applications.
Innovation Solution
A system utilizing self-supervised contrastive learning to embed audio data into a low-dimensional topological space, clustering audio recordings without human interaction, and using density-based clustering to identify anomalous sounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised learning methods are used for anomaly detection, then detection accuracy can be improved, but data collection cost and complexity increase significantly
Solution Approach 1:
The patent inverts the traditional supervised learning approach by using unsupervised learning to detect anomalies. Instead of training the model to recognize specific anomaly patterns through labeled data, the model learns the normal operational patterns and automatically identifies deviations as anomalies. This inversion eliminates the need for costly anomaly data collection while maintaining detection capability.
Solution Approach 2:
The system performs self-supervised learning where the model automatically generates its own training signals from unlabeled audio data. The contrastive learning framework creates positive pairs (similar audio segments) and negative pairs (different audio segments) autonomously, allowing the system to learn without external supervision or manual labeling, thereby reducing data collection complexity.
2Reliability
If supervised learning with labeled anomaly data is used, then model training effectiveness is improved, but data collection time and resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training the model on normal operational audio data before deployment. The contrastive learning framework pre-learns acoustic patterns and relationships during the training phase using readily available normal data, so that when deployed, the model can quickly identify anomalies without requiring time-consuming collection of labeled anomaly data.
Solution Approach 2:
The system creates synthetic training examples through data augmentation techniques, generating varied versions of normal audio recordings with different noise levels, pitch shifts, and time warping. This copying and transformation of existing data allows the model to learn robust patterns without requiring extensive collection of diverse real-world anomaly examples.
3Ease of manufacture
If unlabeled audio data is used for training, then data collection ease is improved, but model learning capability deteriorates
Solution Approach 1:
The patent transforms the learning task by changing the parameter space from classification (requiring labeled data) to contrastive embedding (working with unlabeled data). The model learns to map audio segments into a latent space where similar segments cluster together through contrastive loss optimization, enabling effective learning from unlabeled data while maintaining or improving detection capability.
Solution Approach 2:
The system moves the learning problem from the original audio signal dimension to a latent embedding dimension. By projecting audio segments into a lower-dimensional latent space through the contrastive learning framework, the model captures essential patterns and relationships that facilitate effective anomaly detection even without labeled supervision.
Data Source
AI summary
In some implementations, the device may include receiving a first and second audio dataset. In addition, the device may generate a first, a second, a third, and a fourth audio sample. Moreover, the device may include determining a level of similarity between the first and second audio samples. Also, the device may include combining the first and second audio samples into an audio pair. Further, the device may include training a machine learning model to map audio samples to a latent space visualization in view of time and the similarities between the first and second audio samples to yield a trained machine learning model. In addition, the device may include mapping, by the machine learning model, in the latent space visualization, the third and fourth audio samples, where placement of the third and fourth audio samples depends on the level of similarity between the third and fourth audio samples.