Big Beat Extraction via Weighted Beat Point Sequences
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Solution Overview
Problem
Current technologies lack a method for effectively extracting big beat information from music beat points, which is crucial for enhancing the auditory rhythm of music, particularly in electronic dance music forms like big beat.
Innovation Solution
A method involving acquiring candidate beat cycles, generating and calculating weight for beat point sequences, selecting the maximum weight sequence, and extracting big beat information based on these calculations to identify significant rhythmic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional technology is used for beat point analysis, then basic rhythm detection is possible, but big beat information cannot be extracted
Solution Approach 1:
The patent segments the music beat points into different types (common beats vs. big beats) by analyzing beat cycle patterns. It divides the beat point sequence into candidate beat cycles, then further segments them into sequences with different weights to identify big beats specifically.
Solution Approach 2:
The patent introduces weight parameters to differentiate big beat sequences from common beat sequences. By calculating and comparing weights of different beat point sequences, it transforms the unweighted beat point data into weighted sequences that reveal the big beat information.
2Measurement precision
If all beat points are treated equally, then simple rhythm analysis is achieved, but big beat patterns are lost
Solution Approach 1:
The patent applies local quality by assigning different weights to different beat point sequences based on their characteristics. Instead of treating all beat points uniformly, it gives special attention to sequences that exhibit big beat patterns through weighted scoring.
Solution Approach 2:
The patent performs partial action by focusing computational resources on identifying and weighting sequences that are likely to contain big beat information, rather than analyzing every possible beat pattern with equal depth.
3Measurement precision
If weight calculation is performed for all beat point sequences, then big beat accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the beat point sequences into candidate beat cycles first, then identifies maximum weight sequences from these segments. This hierarchical segmentation reduces the total number of sequences requiring full weight calculation.
Solution Approach 2:
The patent performs weight calculation selectively on candidate beat cycles rather than all possible sequences. It calculates weights for sequences that are candidates for big beats, performing partial action to reduce overall computational load.
Data Source
AI summary
A method for extracting big beat information from music beat points includes: acquiring at least one candidate beat cycle according to beat points of music; generating at least one beat point sequence corresponding to the candidate beat cycle and calculating a weight of the beat point sequence, wherein two adjacent beat points in the beat point sequence take the candidate beat cycle as an interval; selecting a beat point sequence with a maximum weight from the at least one beat point sequence of the candidate beat cycle as a maximum weight sequence of the candidate beat cycle; and acquiring the big beat information according to the maximum weight sequence of the at least one candidate beat cycle and the corresponding weight.