Audio Signal Processing Using Auto-Regressive Residual Modeling
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Solution Overview
Problem
Current audio signal processing techniques require users to set numerous parameters correctly, making the process time-consuming and requiring strong knowledge, especially in sound processing for mixing and mastering.
Innovation Solution
The use of auto-regressive (AR) modeling to create a residual signal from an input audio signal, which is then added to produce a processed output signal, allowing for real-time processing controlled by fewer parameters, and incorporating pre-processing and post-processing options like level adjustment, filtering, and sound effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional sound processing tools (filtering, dynamic processing, sound effects) are used, then audio signal processing capabilities are improved, but the number of user controllable parameters increases significantly
Solution Approach 1:
The patent extracts the essential processing function by using AR modeling to generate a residual signal that captures the key characteristics of the audio signal. Instead of using multiple traditional processing tools with numerous parameters, the invention isolates the core processing need into a single residual signal generation and addition mechanism, dramatically reducing parameter complexity while maintaining processing effectiveness
Solution Approach 2:
The AR-based residual signal processing provides a universal solution that can handle multiple audio processing tasks (enhancement, separation, balancing) through a single mechanism. The residual signal contains predictive error information that is universally applicable to various audio processing scenarios, eliminating the need for separate filtering, dynamic processing, and sound effect tools
2Manufacturing precision
If multiple parameters are set correctly to achieve desired sound processing results, then processing quality is improved, but processing time increases significantly
Solution Approach 1:
The AR modeling process automatically analyzes the input audio signal and generates the residual signal without requiring manual parameter adjustment. The system serves itself by computing the predictive error based on the signal's inherent characteristics, eliminating the need for user experimentation and knowledge while maintaining high processing quality
Solution Approach 2:
The AR model pre-computes the residual signal by analyzing the predictive structure of the audio signal before final processing. This preliminary analysis captures the essential signal characteristics in advance, allowing for quick and accurate processing without time-consuming parameter tuning during the actual processing phase
3Reliability
If strong knowledge and experimentation are required to set parameters correctly, then processing effectiveness is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-analysis through AR modeling to automatically determine the appropriate residual signal characteristics based on the input audio. This eliminates the need for user knowledge and experimentation, as the system independently identifies and processes the essential signal features, making operation simple while maintaining effectiveness
Solution Approach 2:
The residual signal acts as an intermediary that bridges the complex AR modeling process and the simple final processing step. Users interact only with the straightforward residual addition process, while the complex analysis and parameter determination are handled automatically by the AR modeling intermediary
Data Source
AI summary
In an audio signal processing procedure, auto-regressive (AR) modeling is used to create a residual signal from an input audio signal. The residual signal is further added to the input audio in order to produce a processed output audio signal. The AR modeling can be performed frame-by-frame or sample-by-sample employing frequency warped Burg's method.