Self-Supervised Federated Learning for Acoustic Event Detection
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Solution Overview
Problem
Current acoustic event detection systems face challenges in efficiently training machine learning models for recognizing patterns in audio data without annotated labels, and in effectively sharing model updates across devices while maintaining privacy and reducing data transfer costs.
Innovation Solution
The implementation of self-supervised federated learning techniques to train encoder and classifier models for acoustic event detection, where an encoder extracts audio features and a decoder predicts subsequent audio data, with federated learning sharing updates across devices to improve model performance and reduce data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised learning is used for acoustic event detection, then model accuracy can be improved with annotated labels, but data transfer costs and privacy risks increase due to centralized data collection
Solution Approach 1:
The patent introduces a federated learning intermediary layer that enables model training without direct data sharing. Local models are trained on-device using annotated audio data, and only model updates (gradients/parameters) are transmitted to the central server for aggregation. This intermediary mechanism preserves data privacy while achieving collaborative model improvement across multiple devices.
Solution Approach 2:
The patent creates and distributes copies of the acoustic event detection model to multiple client devices. Each device maintains a local copy of the model and performs independent training using its own annotated data. The local model copies are then updated through federated averaging, allowing each device to benefit from collective learning without sharing sensitive audio data.
2Measurement precision
If more audio data is transferred for model training, then model performance improves, but data transfer costs and communication overhead increase
Solution Approach 1:
The patent extracts only the essential model update information (gradients and parameter changes) from the training process, rather than transferring complete datasets or full model states. This extraction of minimal necessary information significantly reduces communication overhead and data transfer costs while maintaining model training effectiveness.
Solution Approach 2:
The patent segments the model training process into local training phases and centralized aggregation phases. During local training, each device independently processes its own data without external communication. Model updates are then segmented and transmitted in small batches to the central server, which aggregates them efficiently. This segmentation minimizes continuous data transfer requirements.
3Loss of information
If self-supervised learning is implemented without annotated labels, then data privacy is preserved, but training effectiveness and feature extraction quality decrease
Solution Approach 1:
The patent merges two learning approaches: self-supervised learning for initial feature extraction and supervised fine-tuning for accuracy improvement. The model first learns general audio representations from unlabeled data through self-supervised pre-training, then leverages available annotated data for targeted fine-tuning on specific acoustic events. This combination preserves privacy benefits while achieving high detection accuracy.
Solution Approach 2:
The patent performs preliminary self-supervised pre-training on unlabeled audio data to establish robust feature extraction capabilities before fine-tuning on annotated data. This preliminary action enables the model to learn general audio patterns and temporal relationships without requiring labeled data, improving subsequent supervised training efficiency and accuracy.
Data Source
AI summary
An acoustic event detection system may employ self-supervised federated learning to update encoder and/or classifier machine learning models. In an example operation, an encoder may be pre-trained to extract audio feature data from an audio signal. A decoder may be pre-trained to predict a subsequent portion of audio data (e.g., a subsequent frame of audio data represented by log filterbank energies). The encoder and decoder may be trained using self-supervised learning to improve the decoder's predictions and, by extension, the quality of the audio feature data generated by the encoder. The system may apply federated learning to share encoder updates across user devices. The system may fine-tune the classifier to improve inferences based on the improved audio feature data. The system may distribute classifier updates to the user device(s) to update the on-device classifier.