Music Analysis Method Beat Point Estimation
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Solution Overview
Problem
Existing techniques for estimating beat points in musical pieces face challenges in achieving high accuracy while maintaining a low calculation amount, with methods either requiring high computational resources for accurate estimation or sacrificing accuracy for reduced complexity.
Innovation Solution
A music analysis method that estimates provisional points using a first process and selects specific points through a second process, utilizing a neural network to calculate probabilities, with candidate selection based on maximizing mutual information to balance accuracy and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a recursive neural network is used to estimate beat points, then estimation accuracy is improved, but calculation amount increases
Solution Approach 1:
The patent segments the estimation process into two distinct stages: a first process that generates provisional beat points and division points, and a second process that refines the estimation using recursive neural networks only on selected points. This segmentation allows the computationally intensive second process to be applied selectively rather than to the entire audio signal, thereby maintaining high estimation accuracy while reducing overall calculation amount.
Solution Approach 2:
The patent applies different processing qualities to different parts of the data. The first process provides a coarse-grained estimation for all potential beat points, while the second process applies high-precision recursive neural network analysis only to selected provisional points and division points. This local application of high-quality processing maintains accuracy where needed while reducing computation in less critical areas.
2Productivity
If simple methods are used to reduce calculation amount, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The first process performs preliminary estimation of beat points and generates division points before the second process is applied. This preliminary action creates a reduced set of candidate points that are then processed by the more accurate but computationally intensive second process, allowing the system to achieve high precision without applying the intensive process to the entire signal duration.
Solution Approach 2:
The first process acts as an intermediary that transforms the raw audio signal into a reduced set of provisional beat points and division points. This intermediary representation serves as a bridge between the full audio signal and the final high-precision estimation, enabling the second process to work on a manageable subset of data while maintaining overall accuracy.
Data Source
AI summary
A music analysis method includes estimating a plurality of provisional points that are candidates for a specific point that has musical meaning in a musical piece from an audio signal of the musical piece by using a first process, selecting a part of a plurality of candidate points, which include the plurality of provisional points and a plurality of division points that divide intervals between the plurality of provisional points, as a plurality of selection points, and estimating a plurality of specific points in the musical piece from a result of calculating a probability that each of the plurality of selection points is the specific point by using a second process which is different from the first process.