Audio Stress Point Detection Using Local Intensity and Extended Sampling
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Solution Overview
Problem
Existing methods for determining stress points in audio data are inaccurate and incomplete, particularly in sync-to-beat video creation, leading to suboptimal sensory experiences.
Innovation Solution
An audio detection method that utilizes a computer device to extract initial stress points based on local maximum sound intensity, volume, and timbre changes, and supplements these with additional points through extended sampling, using sound intensity evaluation functions to verify and refine the stress point set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to determine stress points in audio data, then the detection process is simple, but the accuracy and comprehensiveness of stress point detection is insufficient
Solution Approach 1:
The detection process is divided into multiple independent modules: initial stress point extraction based on local maximum sound intensity, extended sampling to generate supplementary time points, sound intensity evaluation function calculation, and verification against selection conditions. Each module handles a specific aspect of the detection task, improving overall accuracy while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The method performs preliminary extraction of initial stress points based on local maximum sound intensity before conducting the full verification process. This preliminary action identifies candidate points that are likely to be actual stress points, reducing the search space and improving detection accuracy by focusing subsequent analysis on promising candidates.
2Reliability
If only initial stress points based on local maximum sound intensity are used, then the detection method is simple, but the stress point set is incomplete
Solution Approach 1:
Initial stress points are extracted in advance based on local maximum sound intensity, volume, and timbre changes. This preliminary extraction creates a foundation set of candidate points that are then supplemented through extended sampling, ensuring both completeness and efficiency in the detection process.
Solution Approach 2:
The sound intensity evaluation function serves as an intermediary mechanism that bridges the initial stress point extraction and the final verification process. It calculates comprehensive sound intensity values for candidate points and their surrounding regions, enabling the system to identify additional stress points that may not be obvious from local maximum detection alone.
3Measurement precision
If extended sampling is performed to supplement stress points, then the comprehensiveness of stress point detection is improved, but the computational complexity increases
Solution Approach 1:
Extended sampling is performed selectively around identified initial stress points rather than uniformly across the entire audio signal. The sampling radius and intensity evaluation are localized to regions where stress points are likely to occur, improving comprehensiveness while minimizing unnecessary computational overhead in regions with no significant acoustic events.
Solution Approach 2:
The method dynamically adjusts the extended sampling radius based on the local characteristics of the audio signal, such as the density of initial stress points and the variance in sound intensity. By changing the sampling parameters adaptively rather than using fixed values, the system achieves comprehensive coverage while optimizing processing efficiency.
Data Source
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AI summary
Embodiments of this application provide an audio detection method and apparatus, a computer device, and a readable storage medium. The method includes: acquiring a target time point and a reference point of the target time point from target audio data, the reference point referring to a time point with a time difference from the target time point being less than a first difference threshold; obtaining a first sound intensity evaluation value of the target time point according to the audio amplitude value of the target audio at the target time point; obtaining a second sound intensity evaluation value of the reference point according to the audio amplitude value of the target audio at the reference point; determining whether the target time point satisfies a pre-set selection condition based on the first sound intensity evaluation value of the target time point and the second sound intensity evaluation value of the reference point; and if the target time point satisfies the pre-set selection condition, selecting the target time point as a target stress point. This method can more accurately determine stress points in target audio data.