Adaptable Acoustic Model for Zero-Shot Fault Sound Detection
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Solution Overview
Problem
Current digital audio systems in industrial settings struggle to identify new types of product fault sounds due to the need for large amounts of fine-grained labeled data and become outdated quickly, leading to increased maintenance costs and safety risks for workers.
Innovation Solution
Implementing a system that performs contrastive pre-training of an acoustic model, builds a dataset classifier using prompt engineering, and performs zero-shot audio prediction, allowing for the identification of new sound categories using limited labeling data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods are used for fault sound identification, then known fault types can be identified with sufficient accuracy, but the system requires large amounts of fine-grained labeled data and cannot detect new fault types
Solution Approach 1:
The system performs contrastive pre-training on a large corpus of audio data before actual fault detection. This preliminary action enables the model to learn general acoustic patterns and relationships without requiring task-specific labeled data, thereby reducing the need for fine-grained labeled data while maintaining identification accuracy
Solution Approach 2:
The patent introduces text descriptions as an intermediary between audio signals and classification. The audio encoder converts fault sounds into embeddings that are matched against text embeddings generated from fault type descriptions. This intermediary approach allows the system to identify faults without requiring large amounts of labeled audio data for each specific fault type
2Reliability
If traditional classification models are trained on specific fault types, then those known faults can be detected accurately, but the models become outdated quickly when new fault types emerge
Solution Approach 1:
The patent creates a universal acoustic model that can handle multiple fault types through a single system. The contrastive pre-training approach enables the model to learn general acoustic patterns that apply across different fault types, while the text-based classification layer can accommodate new fault types without retraining the entire model, thus achieving both reliability and adaptability
Solution Approach 2:
The system implements dynamic adaptability by allowing new fault types to be added through simple text description updates rather than complete model retraining. The zero-shot classification capability enables the model to adapt to new fault types dynamically, maintaining reliability while being versatile enough to handle emerging fault categories
3Measurement precision
If fine-grained data labeling is performed to improve detection accuracy, then identification precision increases, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The patent uses text descriptions as an intermediary that requires minimal labeling effort. Instead of requiring precise temporal annotations for each fault instance, the system only needs text descriptions of fault types. This intermediary approach maintains identification precision while dramatically reducing the time and labor required for data preparation
Solution Approach 2:
The system uses pre-trained audio encoders and text encoders as copied components from general-purpose models. These pre-trained models serve as reusable building blocks that eliminate the need to train from scratch, reducing both time and computational resources while maintaining precision through the contrastive learning framework
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for building an acoustic model is provided. The present invention may include performing contrastive pre-training of the acoustic model; building a dataset classifier using prompt engineering; performing a prediction process; and performing zero-shot audio prediction using the pre-trained acoustic model.