Large-Scale Acoustic-Language Model for DAS Event Recognition
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Solution Overview
Problem
Existing distributed acoustic sensing (DAS) systems face challenges with large-scale audio data recording, including data storage and privacy concerns, and require user-specific model training for diverse acoustic event recognition, which is inefficient and prone to distribution shifts due to varying fiber environments.
Innovation Solution
A large-scale acoustic-language model with an acoustic encoder and text encoder, pretrained using cross-modal approach, transforms acoustic features into embedding vectors, allowing flexible event classification through language-based interaction and domain adaptation without captions or labels, using background noise and impulse responses for fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed acoustic sensing systems record large-scale audio data for comprehensive acoustic event recognition, then recognition accuracy is improved, but data storage requirements and privacy concerns increase
Solution Approach 1:
The patent extracts only the essential acoustic features and embedding vectors from the raw audio data, storing and processing only these extracted representations rather than the complete audio recordings. This allows maintaining recognition accuracy while dramatically reducing storage requirements.
Solution Approach 2:
The system creates compressed copies of acoustic information in the form of embedding vectors and feature representations, which capture the essential characteristics of acoustic events. These copies can be stored and processed efficiently without needing to retain the original large-scale audio data.
2Measurement precision
If user-specific model training is performed for diverse acoustic event recognition, then recognition accuracy for specific domains is improved, but system complexity and training time increase
Solution Approach 1:
The patent employs a universal acoustic-language model that can handle multiple acoustic event recognition tasks across different domains without requiring separate training for each domain. The model's architecture and pretraining enable it to adapt to various applications through prompt engineering and fine-tuning on minimal domain-specific data.
Solution Approach 2:
The system performs preliminary pretraining of the acoustic-language model on diverse acoustic datasets before deployment. This pretraining establishes a robust foundation that enables the model to handle various acoustic events across different domains, eliminating the need for extensive training for each specific application.
3Productivity
If traditional acoustic models are used without language integration, then processing speed is maintained, but adaptability to diverse acoustic events and user queries is reduced
Solution Approach 1:
The patent merges acoustic processing capabilities with language understanding capabilities in a unified acoustic-language model. The model combines acoustic feature extraction with language generation and understanding components, enabling the system to process acoustic events and respond to user queries in natural language while maintaining efficient processing speeds.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model effectively compresses audio data, ensures privacy, and adapts to different environments, providing flexible and efficient acoustic event recognition with reduced storage needs and improved accuracy.
Implementation Method 1
an acoustic encoder, which encodes acoustic signals into embedding vectors
Implementation Method 2
a text encoder, which encodes text inputs into embedding vectors
Implementation Method 3
Both embedding vectors are then linked in a joint multimodal space using linear projections. The acoustic classification tasks using this model are executed by assessing the similarity between the acoustic and language embedding vectors
Data Source
AI summary
Disclosed are integrated DFOS/DAS systems, methods, and structures that employ a large-scale pretrained recognition model we refer to as an “acoustic-language model”, which is pretrained with natural-language supervision (“contrastive language-audio pretraining”. The acoustic-language model comprises two primary components: an acoustic encoder and a text encoder. These encoders are pretrained using a cross-modal approach on a vast dataset of acoustic features (such as images created from log Mel spectrograms) and their corresponding textual captions. When acoustic features and/or languages are input into their respective encoders within the model, they generate corresponding embedding vectors. Both embedding vectors are then linked in a joint multimodal space using linear projections. The acoustic classification tasks using this model are executed by assessing the similarity between the acoustic and language embedding vectors, essentially evaluating the maximum similarity between the acoustic features and the events described in a specific language.