Audio Signal Processing Filter Bank for High-Definition Audio
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Solution Overview
Problem
Existing audio post-processing technologies face increased computational burdens when adapting to higher sampling rates, such as those used in high-definition audio, which can lead to inefficiencies and increased processing requirements.
Innovation Solution
The method involves filtering audio data into frequency domains, applying signal processing with lower computational complexity to inaudible frequency components, and using an adapted filter bank to process audio data at higher sampling rates, allowing for efficient processing without substantial redesign of existing components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If audio post-processing algorithms are adapted to support higher sampling rates, then high-definition audio processing capability is improved, but computational burden increases
Solution Approach 1:
The audio signal is divided into frequency sub-bands using filter banks. Different signal processing operations are applied to different frequency sub-bands independently. This segmentation allows the system to process only the necessary frequency ranges at full resolution, reducing overall computational burden while maintaining HD audio processing capability.
Solution Approach 2:
Different processing complexities are applied to different frequency regions. Full-resolution processing is applied only where necessary (e.g., audible frequency ranges), while reduced processing is applied to other regions. This local differentiation optimizes computational resources while maintaining audio quality where it matters most.
2Adaptability or versatility
If existing audio post-processing algorithms are updated to support higher sampling rates, then processing capability is improved, but algorithm complexity increases
Solution Approach 1:
The filter bank structure and processing framework are designed to be universal, working with multiple sampling rates (both traditional and high-definition). The same basic algorithmic structure handles different sampling rates by adjusting parameters rather than requiring complete algorithm redesign, reducing overall system complexity.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the input sampling rate. When HD audio is detected, the system activates appropriate processing paths and parameters without requiring a fundamental algorithmic change. This dynamic adaptation maintains capability while controlling complexity.
Data Source
AI summary
In an apparatus configured to perform signal processing on audio data of a first sampling rate, methods disclosed herein comprise receiving audio data of a second sampling rate, the second sampling rate being higher than the first sampling rate. The methods comprise applying filtering to the audio data of the second sampling rate to thereby produce first filtered audio data and second filtered audio data, the first filtered audio data comprising mainly component frequencies which are audible to the human ear, the second filtered audio data comprising mainly components frequencies which are substantially inaudible to the human ear. The methods further comprise applying first signal processing to the first filtered audio data; and applying second signal processing to the second filtered audio data, the second signal processing having a lower computational complexity than the first signal processing. Corresponding apparatus and computer readable media are also disclosed herein.