Energy-Based Audio Pattern Recognition Algorithm
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Solution Overview
Problem
Standard pattern recognition techniques are computationally intensive and fail to effectively account for the specific characteristics of audio signals, making them inefficient for determining similarity between frames in audio signals.
Innovation Solution
An energy-based pattern recognition algorithm that computes the energy of input frames and identifies probable matches based on local maximum energy, then refines these matches by calculating the percentage difference in energy with neighboring frames to determine the best pattern match.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard pattern recognition techniques are used, then classification accuracy can be achieved, but computational complexity becomes excessively high
Solution Approach 1:
The patent extracts and utilizes only the energy characteristic from audio frames, ignoring other complex features. By computing only the energy of each frame and comparing energy values, the system achieves pattern recognition without the computational burden of analyzing multiple signal characteristics simultaneously.
Solution Approach 2:
The patent transforms the audio signal analysis from multiple parameter comparison to single parameter (energy) comparison. By changing the approach from comprehensive signal analysis to energy-only analysis, the computational complexity is dramatically reduced while maintaining effective pattern recognition capability.
2Adaptability or versatility
If standard pattern recognition techniques are used, then general pattern matching is possible, but efficiency for real-time audio processing deteriorates
Solution Approach 1:
The patent employs a self-service mechanism where the energy computation and comparison process is inherently simple and self-contained. Each frame's energy is independently computed and compared against reference energies, enabling efficient real-time processing without complex iterative computations or extensive training requirements.
Solution Approach 2:
By changing from multi-parameter analysis to single energy parameter analysis, the system achieves both adaptability for audio processing and high processing efficiency suitable for real-time applications.
3Measurement precision
If comprehensive signal analysis is performed, then accurate pattern recognition is achieved, but energy consumption increases
Solution Approach 1:
The patent extracts only the energy feature from audio frames, discarding other computational features. This extraction approach maintains sufficient pattern recognition accuracy for audio applications while dramatically reducing the computational energy required compared to comprehensive signal analysis.
Solution Approach 2:
The patent uses simple energy computations that are computationally 'cheap' and can be rapidly performed on each frame. These simple energy calculations consume minimal energy compared to complex pattern recognition algorithms, enabling sustained real-time processing on power-constrained devices.
Data Source
AI summary
An energy-based pattern recognition algorithm receives the input frames of an audio signal and a test frame sequence and returns a best match in the audio signal to the given test frame sequence. The energy of each input frame is computed, and input frames for which the energy is within a predetermined degree of closeness to the local maximum energy within the test frame sequence are identified as probable matches. Probable matches are then eliminated if the respective probable match does not correspond to a local maximum within a respective neighborhood of adjacent frames. The difference between overall energy for frames neighboring the remaining probable matches and the test frame sequence is computed as a percentage, with the minimum percent deviation in energy from the test frame sequence being returned as the best pattern match. Local signal characteristic matching may be employed to refine matching.