MDCT Audio Processing Aliasing Error Correction
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Solution Overview
Problem
The Modified Discrete Cosine Transform (MDCT) based audio processing suffers from time domain aliasing issues due to its critically sampled nature, leading to computational complexity and delay when additional processing is applied, and existing methods to reduce aliasing introduce inaccuracies and additional complexity.
Innovation Solution
A processor calculates aliasing-affected signals using different modification values for each block of spectral values and estimates an aliasing-error signal, which is then combined with the affected signal to produce an aliasing-reduced or aliasing-free signal through a cross-fade process, utilizing parallel inverse transform operations and frequency-time transforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MDCT based processing is used for audio coding, then computational efficiency is improved, but time domain aliasing occurs due to critically sampled nature
Solution Approach 1:
The patent applies the harmful aliasing effect to improve computational efficiency. By allowing aliasing to occur in the MDCT domain and then using a simple subtraction of aliased components, the system achieves efficient processing without requiring complex aliasing reduction mechanisms. The aliasing that would normally be harmful is converted into a manageable error that can be corrected with minimal computation.
Solution Approach 2:
The patent introduces an intermediary approach by processing signals in the MDCT domain rather than directly in the time domain. This intermediate spectral representation allows for efficient processing while managing aliasing effects through the subtraction of aliased components, avoiding the need for complex time-domain aliasing reduction techniques.
2Adaptability or versatility
If additional processing is applied on spectral coefficients, then audio processing capability is improved, but computational complexity and delay increase
Solution Approach 1:
The patent converts the harmful aliasing effect into a beneficial simplification. By allowing aliasing to occur and then using a simple subtraction of aliased components, the system achieves efficient processing without requiring complex aliasing reduction mechanisms, thus reducing computational complexity while maintaining processing capability.
Solution Approach 2:
The patent extracts and removes only the harmful aliasing components from the processed signal. Instead of preventing aliasing through complex means, the system processes the signal allowing aliasing to occur, then extracts and subtracts the aliased components, leaving the desired processed audio signal.
3Reliability
If DFT based post-processing is applied to reduce aliasing, then aliasing robustness is improved, but additional delay is introduced
Solution Approach 1:
The patent converts the harmful aliasing effect into a beneficial simplification that reduces delay. By allowing aliasing to occur in the MDCT domain and then using a simple subtraction of aliased components, the system achieves efficient processing without requiring the additional delay inherent in DFT-based post-processing approaches.
Solution Approach 2:
The patent creates a copy of the aliased signal and subtracts it from the processed signal. This copying approach allows for simple aliasing correction without requiring complex transforms or additional delays, as the aliased components can be directly subtracted from the processed output.
4Device complexity
If real-to-complex transform is used to approximate MDST values, then computational complexity is reduced, but accuracy of MDST coefficients decreases
Solution Approach 1:
The patent converts the need for accurate MDST coefficients into a benefit by using the simpler real-to-complex transform. Instead of requiring precise MDST values, the system uses the approximate values from the real-to-complex transform and achieves acceptable results through the subtraction of aliased components, thus reducing computational complexity while maintaining sufficient accuracy.
Data Source
AI summary
An apparatus for processing an audio signal including a sequence of blocks of spectral values, includes: a processor for calculating an aliasing-affected signal using at least one first modification value for a first block of the sequence of blocks and using at least one different second modification value for a second block of the sequence of blocks and for estimating an aliasing-error signal representing an aliasing-error in the aliasing-affected signal; and a combiner for combining the aliasing-affected signal and the aliasing-error signal such that a processed signal obtained by the combining is an aliasing-reduced or aliasing-free signal.