Digital Audio Signal Noise Reduction via Spectral Comparison
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Solution Overview
Problem
Existing sound recording technologies often result in inadequate recording quality due to noise and interference, particularly in digital recordings made using mobile devices and computers, where signal noise reduction is challenging across a broad spectrum.
Innovation Solution
A method involving signal noise reduction that converts input digital audio signals into frequency domain representations, compares spectral components across adjacent segments, and modifies spectral components based on predetermined conditions to filter out noise, such as high-frequency transient noise, using Discrete Fourier Transform and specific scaling conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital recording is used to transmit and record sound, then recording accessibility and transmission capability are improved, but recording quality deteriorates due to noise and interference
Solution Approach 1:
The input digital signal is divided into multiple overlapping segments, where each segment is processed independently for noise detection. This segmentation allows the system to analyze local spectral characteristics in each segment and apply noise reduction selectively without affecting the entire signal, thereby maintaining recording quality while preserving digital recording accessibility.
Solution Approach 2:
The patent applies different processing thresholds and scaling factors to different spectral components based on their local characteristics. By comparing spectral magnitudes at specific frequency bins across segments and applying localized noise reduction only where anomalies are detected, the system improves recording quality without degrading the overall digital recording capability.
2Manufacturing precision
If complex noise filtering algorithms are applied to improve recording quality, then signal noise reduction is improved, but computational complexity increases
Solution Approach 1:
The patent applies noise reduction processing only to specific spectral components that exhibit anomalous characteristics, rather than processing the entire frequency spectrum uniformly. By detecting peaking, step-up, and step-down conditions at specific frequency bins and applying scaling only where needed, the system achieves effective noise reduction with reduced computational effort compared to full-spectrum processing.
Solution Approach 2:
The system dynamically adjusts processing parameters including scaling factors (lambda values), segment overlap ratios, and detection thresholds based on the local spectral characteristics. This adaptive parameter adjustment allows the algorithm to maintain high noise reduction effectiveness while minimizing computational complexity by adapting to the actual signal conditions rather than using fixed complex processing.
3Manufacturing precision
If spectral components are modified to remove noise, then recording quality is improved, but signal distortion may occur
Solution Approach 1:
The patent uses feedback from spectral magnitude comparisons across adjacent segments to determine whether noise reduction should be applied. By continuously monitoring spectral characteristics and comparing current segment magnitudes with previous and next segments, the system provides feedback to the detection algorithm to distinguish between actual signal content and noise, thereby reducing recording quality improvements while minimizing signal distortion.
Solution Approach 2:
The system performs preliminary detection of spectral anomalies using multiple conditions (peaking, step-up, step-down) before applying any modification. This preliminary action allows the system to identify potential noise components with high confidence before altering the signal, ensuring that only genuine noise is removed while preserving legitimate signal content and preventing distortion.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively reduces signal noise in audio recordings by identifying and modifying spectral anomalies, improving recording quality by removing unwanted noise while being computationally inexpensive and portable across various systems.
Implementation Method 1
converting each of the adjacent segments to a frequency domain representation
Data Source
AI summary
Systems and methods for signal noise reduction. An input digital signal may be partitioned into a series of adjacent segments. The adjacent segments may be converted to a frequency domain representation. A particular spectral component of a particular segment may be compared to a related spectral component of a first segment adjacent the particular segment, and to a related spectral component of a second segment adjacent the particular segment. The particular spectral component may be modified upon a magnitude value of the particular spectral component satisfying at least one of a predetermined set of conditions.