Sound Feature Priority Alignment for Natural Audio
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Solution Overview
Problem
Conventional sound alignment techniques produce unnatural outputs due to uniform treatment of audio frames, which does not align with human perception, leading to perceptible differences and unnatural sound.
Innovation Solution
Sound feature priority alignment techniques identify features in sound data based on similarity and assign priorities according to human perception, dynamically prioritizing frames to align sound data naturally, using sound feature rules that reflect differences in human perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional sound alignment techniques are used to automatically align sound data, then alignment functionality is achieved, but the output sounds unnatural due to uniform treatment of all audio frames
Solution Approach 1:
The patent applies local quality by differentiating the treatment of audio frames based on their perceptual importance. Instead of uniform treatment, the system identifies and prioritizes specific frames (such as phrase onsets and high-energy frames) for alignment, while allowing other frames to have different alignment characteristics. This creates non-uniform alignment that matches human perception and produces more natural-sounding output.
Solution Approach 2:
The patent changes the alignment parameters dynamically based on frame characteristics. By calculating priority values for each frame based on features like energy levels and phrase onset detection, the system adjusts alignment parameters frame-by-frame rather than using a single fixed parameter set. This parameter adaptation enables the alignment to accommodate human perceptual variations and maintain naturalness.
2Measurement precision
If stretching and compressing of audio portions is applied to align sound data, then alignment accuracy is improved, but perceptible differences are introduced making the sound unnatural
Solution Approach 1:
The system applies local quality by concentrating stretching and compressing operations only on specific high-priority frames that are most perceptually important, rather than uniformly applying transformations throughout the entire audio signal. This localized approach minimizes perceptible differences in low-priority regions while maintaining alignment accuracy in critical regions.
Solution Approach 2:
The patent applies partial action by selectively applying alignment transformations only to the extent necessary for high-priority frames. Rather than over-compressing or over-stretching the entire audio signal, the system applies minimal necessary transformations to prioritized frames, reducing the total amount of distortion introduced while maintaining sufficient alignment accuracy.
3Device complexity
If uniform alignment treatment is applied to all audio frames, then processing simplicity is maintained, but human perception consistency is compromised
Solution Approach 1:
The patent segments the audio signal into individual frames and further categorizes them based on detected features such as phrase onsets and energy levels. This segmentation enables the system to apply different alignment strategies to different segments, improving consistency with human perception while maintaining manageable processing complexity through automated feature detection and classification.
Solution Approach 2:
The system implements self-service by automatically detecting and identifying important frames based on their inherent acoustic features. Through automated phrase onset detection and energy level analysis, the system independently determines which frames require prioritized alignment treatment, eliminating the need for manual annotation or complex external guidance while maintaining perception consistency.
Data Source
AI summary
Sound feature priority alignment techniques are described. In one or more implementations, features of sound data are identified from a plurality of recordings. Values are calculated for frames of the sound data from the plurality of recordings. The values are based on similarity of the frames of the sound data from the plurality of recordings to each other, the similarity based on the identified features and a priority that is assigned based on the identified features of respective frames. The sound data from the plurality of recordings is then aligned based at least in part on the calculated values.