Audio Compression Mapping for Low-Range Fidelity Preservation
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Solution Overview
Problem
Existing audio compression methods compromise fidelity, especially in the lower dynamic range, leading to poor sound quality and inefficient storage, while decompression is resource-intensive.
Innovation Solution
Implement a combination of fixed and adaptive mapping schemes to encode audio data, where the lower range uses a fixed mapping and the upper range uses an adaptive mapping, preserving fidelity and reducing storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If audio data is compressed by reducing bit depth from 24-bit to 16-bit or 8-bit format, then storage efficiency is improved, but fidelity of the audio signal is lost
Solution Approach 1:
The audio signal is segmented into multiple frequency bands (e.g., low-frequency band, mid-frequency band, high-frequency band). Each band is processed independently with appropriate bit-depth allocation, allowing critical low-frequency components to retain higher precision while less critical high-frequency components use lower precision, thus resolving the contradiction between storage efficiency and fidelity preservation.
Solution Approach 2:
Different bit-depth precision levels are applied to different frequency bands based on their perceptual importance. The low-frequency band uses higher precision (e.g., 24-bit) to preserve fidelity where human hearing is most sensitive, while high-frequency bands use lower precision (e.g., 16-bit or 8-bit) to improve storage efficiency, achieving local optimization of the fidelity-storage tradeoff.
2Loss of information
If decompression algorithms are implemented to restore compressed audio data, then fidelity can be improved, but computational cost and resource requirements increase
Solution Approach 1:
The audio signal is pre-processed during encoding by separating it into frequency bands and applying optimal quantization to each band before compression. This preliminary organization of data in the frequency domain allows for more efficient storage and reduces the computational burden during decompression, as the structure is already optimized for human perception rather than requiring complex post-processing algorithms.
Solution Approach 2:
Instead of storing the full high-resolution audio signal, the invention stores a simplified representation in the frequency domain with selective precision allocation. During playback, this compact representation is converted back to the time domain using efficient synthesis algorithms, achieving good fidelity with minimal computational resources compared to storing and processing full-resolution data.
3Device complexity
If uniform compression is applied to all portions of the audio signal, then processing simplicity is maintained, but fidelity in critical frequency ranges is degraded
Solution Approach 1:
The audio signal is divided into multiple frequency bands using filters or transform methods. Each band is then compressed with a precision level matched to its perceptual importance, allowing complex differential processing to be organized in a systematic way that maintains overall processing simplicity while significantly improving fidelity in critical low-frequency ranges compared to uniform compression.
Data Source
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AI summary
Systems, methods, and software are disclosed herein for compressing audio data. In an implementation, sampled values of an audio signal have a dynamic range. A division of the dynamic range, into at least a lower range and an upper range, is identified based on a fixed mapping of a lower portion of the sampled values to a subset of quanta in a set of quanta having a depth less than a depth of the sampled values. Then an adaptive mapping of an upper portion of the sampled values to a remaining subset of quanta in the set of quanta is also identified, based at least on a dimension of the upper range. The fixed mapping is used to encode the lower portion of the sampled values based, while the adaptive mapping is used to encode the upper portion of the sampled values based on the adaptive mapping.