Reference Audio Sample Selection for Distortion Removal
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Solution Overview
Problem
Conventional methods for removing audio distortion, such as modulation transfer function (MTF) and iterative distortion removal, face challenges including computational expense, inconsistency in performance across varying audio samples, and environmental adaptability issues, leading to suboptimal speech recognition and interpretation.
Innovation Solution
A method and system that utilizes a trained machine learning model to select a reference audio sample from a library based on the characteristics of a distorted audio sample, applying modulation transfer function (MTF) techniques to effectively remove distortion by determining appropriate filtering parameters and smoothing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional MTF-based distortion removal methods are used, then distortion removal capability is provided, but computational expense increases and performance becomes inconsistent across varying audio samples
Solution Approach 1:
The system dynamically selects reference audio samples based on the characteristics of the distorted audio sample being processed. The reference sample selection adapts to different audio conditions (speech, music, ambient noise) to optimize distortion removal performance for each specific case, resolving the contradiction between consistent performance and computational complexity by choosing the most efficient reference for each scenario
Solution Approach 2:
The system changes the parameter of reference sample selection based on audio characteristics. By identifying whether the audio is speech, music, or contains ambient noise, and selecting appropriate reference samples accordingly, the system optimizes the MTF-based distortion removal process for different audio types, improving reliability without uniformly increasing computational complexity
2Measurement precision
If reference audio samples are selected for MTF operations, then distortion removal accuracy improves, but the system complexity increases due to reference selection and characteristic extraction
Solution Approach 1:
The system performs preliminary extraction of audio characteristics (such as spectral features, temporal patterns) before selecting reference samples and performing MTF operations. This preliminary analysis enables accurate reference selection that directly improves distortion removal accuracy while keeping the overall system complexity manageable by doing the heavy lifting upfront
Solution Approach 2:
The system uses extracted audio characteristics as an intermediary between the distorted audio sample and the reference sample selection process. These characteristics serve as a bridge that enables accurate matching without requiring direct complex comparison between all possible reference samples, thus improving accuracy while controlling system complexity
3Adaptability or versatility
If processing is performed frame-by-frame, then adaptability to varying audio characteristics improves, but processing time increases
Solution Approach 1:
The system applies partial processing at the frame level by selecting reference samples based on frame characteristics, then applies the MTF operations more efficiently across the entire audio sample. This partial adaptation approach maintains adaptability to varying characteristics while reducing the overall processing time compared to fully frame-by-frame processing
Data Source
AI summary
A method includes receiving, by a processing device, an audio sample of a distorted voice. The method further includes extracting a first set of characteristics from the audio sample. The method further includes selecting a second set of characteristics. The second set of characteristics is associated with one of a set of reference audio samples. The method further includes generating an undistorted audio sample. The undistorted audio sample is based on the distorted audio sample, the first set of characteristics, and the second set of characteristics.