Sinusoidal Trajectory Encoding with Dynamic Segment Length
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audio signal encoding methods based on sinusoidal or sinusoidal-noise models are inefficient due to not considering long-term stability and predictability of sound components, resulting in high bit requirements for maintaining quality.
Innovation Solution
The method involves adjusting the segment length of sinusoidal trajectories individually for each trajectory, performing digital transforms on longer segments, and using entropy encoding with nonlinear operations and energy parameter replacement to reduce data rate, while maintaining quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sinusoidal encoders use fixed-length segments for digital transform, then the encoding process is simple, but the compression efficiency is low and bit requirements are high
Solution Approach 1:
The patent applies dynamic segment length adjustment for each sinusoidal trajectory, allowing the segment length to vary based on the characteristics of individual trajectories rather than using a fixed length for all trajectories. This dynamic adaptation improves compression efficiency by optimizing the representation for each specific trajectory while managing the increased computational complexity.
Solution Approach 2:
The patent implements local quality optimization by treating each sinusoidal trajectory individually with its own optimized segment length, rather than applying a uniform approach to all trajectories. This allows each local region (trajectory) to be encoded with the specific characteristics needed for optimal compression, improving overall efficiency.
2Loss of information
If sinusoidal encoders use longer segments for digital transform, then compression ratio improves, but processing complexity increases
Solution Approach 1:
The patent segments the audio signal into multiple frames and further divides trajectories into segments for digital transform. By using segmentation with variable lengths adapted to each trajectory's characteristics, the method achieves better compression ratios while managing processing complexity through structured organization of the transform process.
Solution Approach 2:
The patent changes the parameter of segment length dynamically for each trajectory based on its characteristics. This parameter adaptation allows longer segments to be used where beneficial for compression while avoiding excessive processing complexity by adjusting the segment length to match the specific needs of each trajectory.
3Productivity
If fixed segment length is used for all trajectories, then processing is simpler, but compression efficiency is reduced
Solution Approach 1:
The patent applies local quality optimization by assigning different segment lengths to different sinusoidal trajectories based on their individual characteristics. This local adaptation improves compression efficiency by optimizing each trajectory's representation while the overall structure maintains manageable complexity through systematic processing.
Solution Approach 2:
The patent introduces dynamics into the segment length selection process, allowing the segment length to be adjusted based on the characteristics of each trajectory. This dynamic approach improves compression efficiency by adapting to the varying properties of different trajectories while maintaining processing organization through structured implementation.
Data Source
AI summary
The invention concerns an audio signal encoding method comprising the steps of: collecting the audio signal samples, determining sinusoidal components in subsequent frames, estimation of amplitudes and frequencies of the components for each frame, merging thus obtained pairs into sinusoidal trajectories, splitting particular trajectories into segments, transforming particular trajectories comprising of their amplitude and frequency variations to the frequency domain by means of a digital transform performed on segments longer than the frame duration, quantization and selection of transform coefficients in the segments, entropy encoding, and outputting the quantized coefficients as output data. The method is characterized in that the length of the segments into which each trajectory is split is individually adjusted in time for each trajectory.