Audio Decoder Window Switching for Transient Frame Reproduction
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Solution Overview
Problem
Existing audio decoding systems face challenges in achieving improved performance, quality, and efficiency in reproducing audio signals, particularly in handling transient frames and varying resolution modes.
Innovation Solution
The method involves retrieving audio data from a bit stream by applying codebooks to specified ranges of quantization indexes and identifying sequences of windows within a frame based on window information, allowing for efficient decoding and improved reproduction of audio signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single resolution mode is used for audio decoding, then the system complexity is reduced, but the adaptability to different audio content (transient vs. steady-state) deteriorates
Solution Approach 1:
The filter bank resolution is made dynamic by switching between different resolution modes based on transient detection. The system transitions from a static single-resolution design to a dynamic multi-resolution design where the resolution adapts to the audio content characteristics, specifically switching to higher resolution for transient frames and lower resolution for steady-state frames.
Solution Approach 2:
The system changes the resolution parameter of the filter bank based on the detected audio content type. By monitoring transient characteristics and adjusting the filter bank resolution accordingly, the system optimizes decoding performance for different audio scenarios without requiring a completely different decoder architecture.
2Manufacturing precision
If higher resolution filtering is applied to all frames, then the audio quality is improved, but the computational efficiency deteriorates
Solution Approach 1:
Different resolution levels are applied to different portions of the audio signal based on local characteristics. Transient frames receive higher resolution filtering to preserve detail, while steady-state frames use lower resolution filtering to reduce computational load. This local adaptation of quality levels optimizes the overall efficiency-quality tradeoff.
Solution Approach 2:
Instead of applying full high-resolution filtering to all frames, the system applies partial high-resolution processing only where necessary (transient frames). This partial action approach maintains audio quality where needed while avoiding the excessive computational cost of universal high-resolution processing.
3Adaptability or versatility
If variable resolution modes are implemented, then the adaptability to different audio content is improved, but the device complexity increases
Solution Approach 1:
Transient detection is performed in advance of the filtering operation, allowing the system to pre-determine the appropriate resolution mode before processing the audio frame. This preliminary analysis simplifies the overall system architecture by separating the detection and filtering stages, with the detection results guiding the subsequent filtering operation.
Solution Approach 2:
The filter bank is designed to perform multiple resolution modes using a unified structure. Rather than implementing separate filter banks for different resolutions, the system uses a single filter bank that can operate in multiple resolution modes, reducing overall system complexity while maintaining adaptability.
Data Source
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AI summary
Provided are, among other things, systems, methods and techniques for decoding an audio signal from a frame-based bit stream. Each frame includes processing information pertaining to the frame and entropy-encoded quantization indexes representing audio data within the frame. The processing information includes: (i) code book indexes, (ii) code book application information specifying ranges of entropy-encoded quantization indexes to which the code books are to be applied, and (iii) window information. The entropy-encoded quantization indexes are decoded by applying the identified code books to the corresponding ranges of entropy-encoded quantization indexes. Subband samples are then generated by dequantizing the decoded quantization indexes, and a sequence of different window functions that were applied within a single frame of the audio data is identified based on the window information. Time-domain audio data are obtained by inverse-transforming the subband samples and using the plural different window functions indicated by the window information.