Wireless Acoustic Recognition Network Using Edge Embedding Vectors
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Solution Overview
Problem
Existing acoustic recognition models face challenges in large-scale deployment due to data size and privacy concerns, are limited to predefined event classes, and struggle to differentiate between acoustically similar but contextually distinct sounds, making them inflexible and prone to errors.
Innovation Solution
A wireless acoustic sensor network with a compressed acoustic-language model, utilizing a pretrained acoustic and text encoder, optimized for edge devices, generates embedding vectors that are wirelessly transmitted to a central node for processing, allowing for customizable event classification and mitigation of privacy issues through vector-based analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio data is transmitted directly to a central server for processing, then acoustic event recognition can be performed, but data size becomes excessively large and privacy concerns arise
Solution Approach 1:
The system segments the acoustic recognition task by deploying distributed acoustic event recognition models on individual edge devices (microphones/sensors). Each device independently processes its own audio data locally, extracting event information without needing to transmit raw audio to a central server. This segmentation eliminates the data size problem while maintaining recognition capability.
Solution Approach 2:
The patent introduces an intermediary acoustic event recognition model that acts as a bridge between raw audio data and event information. This model processes audio data locally on edge devices, transforming it into compact event representations (such as event types, timestamps, and confidence scores) before transmission, thereby reducing data volume while preserving essential acoustic information.
2Measurement precision
If audio data is centralized and stored on a server, then processing can be performed, but privacy concerns increase due to potential overhearing and electronic preservation of conversations
Solution Approach 1:
The system extracts only the essential event information (event type, timestamp, confidence score) from the audio data using local acoustic recognition models, leaving the sensitive raw audio content on edge devices. This extraction approach provides the necessary recognition capability while eliminating privacy risks associated with centralized storage of complete audio recordings.
Solution Approach 2:
Instead of transmitting and storing original audio data, the system creates simplified copies in the form of structured event information (JSON format containing event type, start time, end time, and confidence). These copies contain only the necessary analytical results without any identifiable human voice or conversation content, thus maintaining privacy while enabling processing.
3Quantity of substance
If acoustic recognition models use predefined event classes, then data compression is achieved, but flexibility to differentiate between contextually distinct sounds is lost
Solution Approach 1:
The patent implements dynamic event classification where the acoustic recognition model can adapt to different event types and contexts. The model uses a comprehensive event ontology that allows for fine-grained classification (e.g., distinguishing between different types of shots, alarms, or environmental sounds) while maintaining data compression. The system can dynamically adjust classification granularity based on application needs.
Solution Approach 2:
The system changes the parameter of event classification from fixed predefined classes to a more flexible hierarchical structure. The acoustic recognition model outputs structured event information that includes event type, temporal information, and confidence scores, allowing for both compressed representation and fine-grained differentiation. This parameter change enables the system to maintain data efficiency while improving adaptability to various acoustic contexts.
Data Source
AI summary
A wireless acoustic sensor network with a compressed acoustic-language model/acoustic recognition model which is pretrained with a language model contrastive language-audio pretraining which involves two main components: an acoustic encoder and a text encoder which are trained on a large dataset of acoustic features and their textual captions. Inputting acoustic features or language generates embedding vectors. These vectors are linked in a joint latent space. Acoustic classification tasks involve assessing similarity between these embedding vectors. Users interact with the model using language input for classification. The architecture of this model permits the segregation of the pre-trained framework into distinct acoustic and text encoders, enabling their deployment across various devices, for instance, positioning the acoustic encoder on edge nodes and the text encoder on a central node.