Audio Fingerprinting via Dynamic Sample Length Adaptation
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Solution Overview
Problem
Conventional audio identification schemes face inefficiencies due to fixed sample lengths for generating audio fingerprints, leading to unnecessary delays for complex signals and false positives for simple signals, as they do not adapt to the inherent complexity of the audio signals.
Innovation Solution
An audio identification system that determines the sample length based on the complexity of the audio signal through autocorrelation, using shorter lengths for complex signals and longer lengths for less complex signals to generate test audio fingerprints, thereby optimizing identification speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed sample length is used for generating audio fingerprints, then the identification process is simplified and standardized, but identification accuracy decreases for simple signals and identification speed decreases for complex signals
Solution Approach 1:
The patent implements dynamic sample length selection by analyzing signal characteristics (energy, zero-crossing rate, spectral features) to adaptively determine the optimal sample length for each audio signal. This replaces the static fixed-length approach with a dynamic system that adjusts to signal complexity, thereby improving identification accuracy without sacrificing operational simplicity.
Solution Approach 2:
The patent changes the parameter of sample length from a fixed constant to a variable determined by signal analysis. By computing signal characteristics and selecting sample lengths from a set of possible values based on these characteristics, the system optimizes the balance between standardized processing and accurate identification for different signal types.
2Reliability
If a longer sample length is used for generating audio fingerprints, then identification accuracy improves for simple signals, but identification delay increases for all signals
Solution Approach 1:
The patent dynamically adjusts the sample length parameter based on analyzed signal characteristics. For simple signals with repetitive patterns, shorter sample lengths are selected to reduce delay. For complex signals requiring more features for accurate identification, longer sample lengths are chosen. This parameter adaptation resolves the trade-off between accuracy and delay.
Solution Approach 2:
The patent segments the audio signal into frames and analyzes characteristics of each frame to determine appropriate sample length. By processing signals in manageable segments and selecting optimal lengths based on local signal properties, the system achieves accurate identification without unnecessarily long processing delays.
3Productivity
If a shorter sample length is used for generating audio fingerprints, then identification speed improves for complex signals, but false positives increase for simple signals
Solution Approach 1:
The patent adjusts the sample length parameter dynamically based on signal complexity analysis. For complex signals where speed is critical and distinctive features are abundant, shorter sample lengths are selected to improve identification speed. For simple signals where false positives are a concern, longer sample lengths provide sufficient distinctive features for reliable identification, thereby reducing false positive rates.
Solution Approach 2:
The patent implements a feedback mechanism where signal characteristics are analyzed and used to determine optimal sample length. This feedback loop ensures that the sample length is appropriately matched to signal properties, balancing speed and accuracy by selecting shorter lengths for complex signals and longer lengths for simple signals.
Data Source
AI summary
An audio identification system accounts for an audio signal's complexity when generating a test audio fingerprint for identification of the audio signal. In particular, the audio identification system determines a complexity of an audio signal to be fingerprinted. For example, the audio signal's complexity may be determined by performance of an autocorrelation on the audio signal. Based on the determined complexity, the audio identification system determines a length of a sample of the audio signal used to generate a test audio fingerprint. A sample having the length is then obtained and used to generate a test audio fingerprint for the audio signal. The test audio fingerprint may be compared to a set of reference audio fingerprints to identify the audio signal.