Automated Speaker Signal Scoring via Frequency Bin Analysis
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Solution Overview
Problem
Existing methods for testing speaker quality are subjective and lack quantifiability, as they rely on human evaluation of imperfect acoustic signals with jagged waveforms and multiple peaks, making it difficult to objectively measure and evaluate speaker performance.
Innovation Solution
A system that captures test signals from a device under test and compares them to reference signals, slicing both into frequency bins for comparison, calculating a performance score based on the differences, and generating a report with graphical illustrations of performance grades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human listeners are used to evaluate speaker quality, then subjective assessment of acoustic signals is possible, but the quality measurement becomes non-quantifiable and subjective
Solution Approach 1:
The patent replaces the human auditory system (biological/mechanical) with an automated digital signal processing system. The system captures acoustic signals, converts them to digital data, performs spectral analysis, and automatically compares results against reference values, eliminating subjective human judgment while providing quantifiable measurements through computational algorithms
Solution Approach 2:
The patent introduces spectral analysis data and reference comparison metrics as intermediaries between the acoustic signal and the evaluation result. Instead of direct human listening, the system uses spectral characteristics (frequency bins, amplitude values) as mediators to objectively quantify speaker performance through measurable parameters
2Measurement precision
If automated signal processing is implemented, then quantifiable measurement is achieved, but the system complexity increases
Solution Approach 1:
The patent segments the acoustic signal into discrete frequency bins through spectral analysis, and divides the evaluation into multiple measurable parameters (amplitude, frequency response, distortion metrics). This segmentation transforms a complex continuous signal evaluation into manageable discrete components that can be processed and compared systematically
Solution Approach 2:
The patent transforms the acoustic signal from time-domain representation to frequency-domain representation through spectral analysis. By changing the parameter space (from temporal waveforms to spectral coefficients), the system enables automated comparison against reference values and facilitates quantifiable measurement through mathematical operations
Data Source
AI summary
Embodiments described herein generally relate to measuring and evaluating a test signal generated by a device under test (DUT). In particular, the test signal generated by the DUT may be compared to a reference signal and scored based on the comparison. For example, a method may include: capturing a test signal from a device under test; splicing the test signal into a plurality of test audio files based on a plurality of frequency bins; at each frequency bin, comparing each of the plurality of test audio files to a corresponding reference audio file from among a plurality of reference audio files, the plurality of reference audio files being associated with a reference signal; and calculating a performance score of the device under test based on the comparisons.