Tone Frame Detector Using Error-Criteria Matching
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Solution Overview
Problem
Existing digital speech communication systems struggle to reliably detect and extract tone frames and other non-voice data under degraded channel conditions, particularly in systems like P25, DMR, and dPMR, where vocoders are used to compress voice data, leading to challenges in maintaining signal integrity and data transmission quality.
Innovation Solution
A tone frame detector is employed to analyze frames of bits in a voice bit stream, utilizing redundancy provided by repeated tone indices and forward error correction, to identify and extract tone frames even in highly degraded channel conditions, by comparing bits to multiple sets of tone data and selecting the closest match based on error criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a vocoder is used to compress voice data to reduce bit rate, then data transmission efficiency is improved, but the ability to reliably detect and extract tone frames under degraded channel conditions deteriorates
Solution Approach 1:
The patent applies preliminary action by inserting training sequences and tone markers into the compressed bit stream before transmission. These predefined patterns serve as reference signals that enable reliable tone frame detection at the receiver, even when the compressed voice data is corrupted by channel degradation. The training sequences are processed in advance to create detectable patterns that survive the compression and transmission process.
Solution Approach 2:
The patent uses training sequences and tone markers as intermediary elements that facilitate the detection process. These intermediaries are embedded within the compressed bit stream and serve as bridge signals between the transmitted data and the detection algorithm, enabling reliable tone frame identification without requiring direct analysis of the compressed voice data alone.
2Quantity of substance
If compression ratios are increased to conserve RF spectrum, then bandwidth utilization is improved, but the complexity of detecting non-voice data increases
Solution Approach 1:
Training sequences and tone markers act as intermediary reference signals that simplify the detection process. Instead of analyzing complex compressed voice data directly, the detector can correlate received signals with these known training sequences, reducing computational complexity while enabling reliable detection of non-voice data in highly compressed bit streams.
Solution Approach 2:
The patent uses copying by transmitting known training sequences and tone markers that are exact replicas of predefined patterns. The receiver detects these copied patterns through correlation, which is computationally simpler than analyzing arbitrary compressed data. This copying approach enables efficient detection without requiring complex analysis of the compressed voice content.
3Reliability
If forward error correction is applied to protect tone frames, then detection reliability is improved, but the bit rate increases
Solution Approach 1:
The patent applies local quality by providing enhanced protection (forward error correction and repetition) only to critical tone frames and training sequences, while voice data maintains standard compression. This localized approach ensures reliable tone detection without applying excessive error correction to all data, thereby maintaining overall transmission efficiency while protecting the most critical signaling information.
Solution Approach 2:
The patent uses partial action by applying error correction and repetition only to tone markers and training sequences rather than the entire bit stream. This selective application provides sufficient protection for tone frame detection while avoiding the overhead of full-error correction across all data, thus balancing reliability improvement with transmission efficiency.
Data Source
AI summary
Tone data embedded in a voice bit stream that includes frames of non-tone bits and frames of tone bits is detected and extracted by selecting a frame of bits, analyzing the selected frame of bits to determine whether it is a frame of tone bits, and, when it is a frame of tone bits, extracting tone data from it. Analyzing the selected frame includes comparing bits of the selected frame to sets of tone data to produce error criteria representative of differences between the selected frame and each of multiple sets of tone data. Based on the error criteria, a set of tone data that most closely corresponds to the bits of the selected frame is selected. When the error criteria corresponding to the selected set of tone data satisfies a set of thresholds, the selected frame is designated as a frame of tone bits.