Autonomous Intelligent Radio Using Machine Learning for RF Audio Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Metadata-based radio searching is limited by disambiguation issues, reliance on available metadata, and adherence to specific communication protocols, making it ineffective for identifying audio characteristics across the RF spectrum, especially in unregulated channels or without metadata.
Innovation Solution
The use of machine-learned classifiers, such as deep neural networks, to extract and analyze audio characteristics directly from RF signals, allowing for the identification of speech-related content regardless of metadata availability or communication protocols, and enabling search across the entire RF spectrum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If metadata-based radio searching is used, then search functionality is provided, but search accuracy deteriorates due to disambiguation issues and metadata limitations
Solution Approach 1:
The patent replaces metadata-based searching with audio signal processing and machine learning classification. Instead of relying on metadata tags that suffer from disambiguation issues, the system directly analyzes audio characteristics (speech, music, silence, noise) from the RF signals themselves using trained classifiers, thereby eliminating the information loss inherent in metadata abstraction.
2Measurement precision
If machine learning-based audio classification is used, then search accuracy improves, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning classifiers offline with labeled audio data before deployment. The classification models are trained in advance to recognize various audio characteristics (speech, music, silence, noise), so that during actual operation, the system can perform rapid classification without requiring complex real-time processing, thus managing operational complexity while maintaining high accuracy.
3Measurement precision
If classification models are trained with labeled data, then classification accuracy improves, but data preparation time and complexity increase
Solution Approach 1:
The patent performs data preparation and model training as preliminary actions before deployment. Labeled training data is collected and prepared in advance, and classification models are trained offline using frameworks like TensorFlow or PyTorch. This upfront investment in time and computational resources enables rapid, accurate classification during actual operation without requiring real-time data labeling or training.
Data Source
AI summary
Embodiments of the disclosed technologies include finding content of interest in an RF spectrum by automatically scanning the RF spectrum; detecting, in a range of frequencies of the RF spectrum that includes one or more undefined channels, a candidate RF segment; where the candidate RF segment includes a frequency-bound time segment of electromagnetic energy; executing a machine learning-based process to determine, for the candidate RF segment, signal characterization data indicative of one or more of: a frequency range, a modulation type, a timestamp; using the signal characterization data to determine whether audio contained in the candidate RF segment corresponds to a search criterion; in response to determining that the candidate RF segment corresponds to the search criterion, outputting, through an electronic device, data indicative of the candidate RF segment; where the data indicative of the candidate RF segment is output in a real-time time interval after the candidate RF segment is detected.