Audio Encoding with Transient-Adaptive Filter Bank Compression
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Solution Overview
Problem
Existing audio encoding techniques lack improvements in performance, quality, and compression, particularly in efficiently representing audio data using variable bit lengths and resolutions.
Innovation Solution
The method involves dividing audio signals into frames, identifying transients, performing multi-resolution filter bank analysis, and generating variable-length code book segments based on quantization data magnitudes to encode audio signals more accurately with fewer bits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If variable-resolution filter bank analysis is performed on frames containing transients, then transient encoding accuracy is improved, but computational complexity increases
Solution Approach 1:
The audio signal is divided into frames, and each frame is further segmented into subbands through filter bank analysis. This segmentation allows different processing strategies to be applied to different portions of the signal, improving transient encoding accuracy while managing computational complexity through localized processing.
Solution Approach 2:
The filter bank uses variable resolution where the number of subbands and analysis parameters dynamically adapt based on the detected transient characteristics in each frame. This dynamic adjustment optimizes encoding accuracy for transients while avoiding unnecessary computational overhead in non-transient regions.
2Productivity
If variable numbers of bits are used for quantization based on psychoacoustical model, then encoding efficiency is improved, but quantization complexity increases
Solution Approach 1:
The quantization process dynamically changes the number of bits allocated to different subbands based on psychoacoustical masking thresholds and signal characteristics. This parameter adaptation improves encoding efficiency by allocating more bits to perceptually important regions while using fewer bits for less critical regions, with the complexity managed through standardized psychoacoustical models.
3Quantity of substance
If quantization data are grouped into variable-length segments based on magnitudes, then compression ratio is improved, but segmentation complexity increases
Solution Approach 1:
Quantization data are grouped into variable-length segments where the segment boundaries and characteristics are determined by local magnitude characteristics of the quantization data. This local adaptation allows more compact representation in regions with similar characteristics while maintaining flexibility for varying data patterns, improving compression ratio with manageable segmentation complexity.
Data Source
AI summary
Provided are, among other things, systems, methods and techniques for encoding an audio signal, in which is obtained a sampled audio signal which has been divided into frames. The location of a transient within one of the frames is identified, and transform data samples are generated by performing multi-resolution filter bank analysis on the frame data, including filtering at different resolutions for different portions of the frame that includes the transient. Quantization data are generated by quantizing the transform data samples using variable numbers of bits based on a psychoacoustical model, and the quantization data are grouped into variable-length segments based on magnitudes of the quantization data. A code book is assigned to each of the variable-length segments, and the quantization data in each of the variable-length segments are encoded using the code book assigned to such variable-length segment.