Landmark-Based Convolution for Sparse Sound Pattern Identification
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Solution Overview
Problem
Conventional pattern identification techniques in sound data are resource-intensive and inefficient, especially when dealing with sparse data, making them unsuitable for real-time scenarios due to unnecessary computational resource consumption.
Innovation Solution
The use of convolution-based pattern identification that represents sound data with frequency and time coordinates and energy values, allowing for the identification of irregularly positioned patterns through Nonnegative Factor Deconvolution (NFD), which minimizes processing of sparse data portions and leverages compact representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional matrix representation techniques are used for pattern identification in sound data, then pattern matching can be performed, but computational resource consumption increases significantly and processing time increases
Solution Approach 1:
The patent extracts only the essential information from sound data by representing it as a set of Landmarks with coordinates and energies, filtering out redundant matrix data. This extraction reduces the data representation to only what is necessary for pattern identification, significantly reducing computational resource consumption while maintaining identification accuracy.
Solution Approach 2:
The patent transforms the traditional 2D matrix representation into a multi-dimensional landmark representation that includes time coordinates, frequency coordinates, and energy values. This dimensional transformation allows for more efficient pattern matching by directly operating on the essential features rather than processing entire matrices, reducing computational complexity.
2Measurement precision
If conventional matrix representation techniques are used for pattern identification in sound data, then pattern matching can be performed, but processing time increases making real-time scenarios infeasible
Solution Approach 1:
By extracting only the essential Landmark information (coordinates and energies) from the sound data and discarding redundant matrix representations, the patent dramatically reduces the amount of data that needs to be processed. This extraction enables real-time pattern identification by eliminating unnecessary computational operations.
Solution Approach 2:
The patent replaces the traditional mechanical matrix-based pattern matching process with a convolution-based approach that operates directly on the compressed landmark representation. This substitution of the processing mechanism enables faster computation and real-time performance while maintaining pattern identification accuracy.
3Reliability
If sparse sound data with most frequency energies close to zero is processed using conventional techniques, then complete analysis can be performed, but computational resources are wasted on unnecessary processing
Solution Approach 1:
The patent extracts only the non-zero energy Landmarks from the sound data, completely ignoring the zero-energy portions. This extraction approach ensures that computational resources are not wasted on processing sparse/zero data while maintaining complete analysis of the meaningful content, achieving both reliability and efficiency.
Solution Approach 2:
The patent applies different processing quality to different parts of the data by focusing computational effort only on the local regions where energy exists (the Landmarks) rather than uniformly processing the entire matrix. This local quality approach ensures reliable analysis of meaningful data while avoiding wasteful processing of sparse regions.
Data Source
AI summary
Pattern identification using convolution is described. In one or more implementations, a representation of a pattern is obtained that is described using data points that include frequency coordinates, time coordinates, and energy values. An identification is made as to whether sound data described using irregularly positioned data points includes the pattern, the identifying including use of a convolution of the frequency or time coordinates to determine correspondence with the representation of the pattern.