Sound Recognition Using Peak Value Statistical Analysis
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Solution Overview
Problem
Conventional sound recognition technologies face high computational burdens and poor accuracy due to oversensitivity to noise, making them inefficient for real-time applications in portable devices.
Innovation Solution
A sound recognition apparatus and method in portable devices that extracts peak values from sound inputs, calculates statistical data, and compares them to pre-stored base sounds using a probability distance model or neural network, reducing noise interference and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sound recognition uses phoneme segmentation and phoneme awareness, then recognition accuracy is improved, but computational burden increases significantly
Solution Approach 1:
The patent extracts only the essential acoustic features (peak values) from sound signals, discarding unnecessary phoneme segmentation steps. By focusing on extracting peak values and their statistical characteristics rather than performing complete phoneme analysis, the system achieves acceptable recognition accuracy with significantly reduced computational burden.
Solution Approach 2:
The patent changes the parameter representation from detailed phoneme sequences to simplified statistical parameters (mean, standard deviation, skewness, kurtosis) of peak values. This parameter transformation reduces the complexity of sound recognition while maintaining sufficient accuracy for practical applications.
2Manufacturing precision
If conventional sound recognition separates sound into distinct phonemes, then recognition detail is improved, but noise sensitivity increases
Solution Approach 1:
The patent extracts only the most robust acoustic features (peak values) that are less sensitive to noise interference. By taking out only the essential peak value information and ignoring detailed phoneme structures, the system reduces noise sensitivity while maintaining sufficient recognition detail.
Solution Approach 2:
The patent uses simple statistical parameters (mean, standard deviation, skewness, kurtosis) as disposable features for recognition, rather than investing in complex phoneme analysis that is more vulnerable to noise. These simple statistical features provide robust noise tolerance.
3Productivity
If sound recognition processes are simplified, then computational burden is reduced, but recognition accuracy deteriorates
Solution Approach 1:
The patent transforms sound recognition into parameter extraction and statistical analysis (mean, standard deviation, skewness, kurtosis of peak values). This parameter change enables simplified processing while maintaining adequate recognition accuracy through multi-parameter statistical comparison.
Solution Approach 2:
The patent adds statistical dimensionality (multiple statistical parameters: mean, standard deviation, skewness, kurtosis) to the simplified peak value extraction process. This dimensional enrichment compensates for the simplification, maintaining recognition accuracy while enabling efficient processing.
Data Source
AI summary
Provided are an apparatus and a method capable of recognizing a sound through a reduced burden of computations and a noise-tolerant technique. The sound recognition apparatus in a portable device includes a memory unit that stores at least one base sound and a sound input unit that receives a sound input. The sound recognition apparatus also includes a control unit that receives the sound input from the sound input unit, extracts peak values of the sound input, calculates statistical data by using the peak values, and determines whether the sound input is equal to a base sound by using the statistical data.