Repeatogram Audio Partition Correlation for Music Pattern Analysis
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Solution Overview
Problem
Existing music composition analysis methods fail to effectively identify and visualize repeated segments within an audio track, limiting the ability to understand and represent self-similar structures in musical compositions.
Innovation Solution
A system that compares partitions of an audio track to determine correlations, constructing a 'Repeatogram' dataset by plotting the time of most correlated partitions, allowing for the visualization of repeated segments across the track duration using a client-server architecture and components like audio track, partition, comparison, correlation, and repeatogram components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio tracks are partitioned and compared to identify repeated segments, then the ability to detect and visualize self-similar structures is improved, but the computational complexity and processing time increase
Solution Approach 1:
The audio track is divided into multiple partitions or segments that can be independently compared. This segmentation allows the system to identify repeated patterns by comparing individual segments rather than analyzing the entire audio track as a single unit, improving detection precision while managing computational complexity through structured division of the analysis task.
2Reliability
If all partitions of an audio track are compared to identify correlated segments, then the completeness of repeated segment identification is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing the audio track into standardized partitions and establishing comparison criteria before the actual correlation analysis. This preliminary structuring enables more efficient subsequent comparisons, maintaining high identification reliability while reducing overall processing time through optimized analysis workflow.
3Measurement precision
If a minimum distance parameter is set to filter partition comparisons, then the accuracy of repeated segment identification is improved, but the quantity of detected repetitions decreases
Solution Approach 1:
The system employs a minimum distance parameter as a configurable threshold that balances identification accuracy and detection quantity. By adjusting this parameter, users can optimize the trade-off between precision (reducing false positives) and sensitivity (capturing all valid repetitions), allowing flexible adaptation to different musical compositions and analysis requirements.
Data Source
AI summary
A dataset representing repeated sounds within a musical composition recorded on an audio track may be constructed. An audio track duration of an audio track may be partitioned into partitions of a partition size. A current partition may be compared to remaining partitions of the audio track. Audio information for the current partition may be correlated to audio information for remaining partitions to determine a correlated partition for the current partition from among the remaining partitions of the track duration. The correlated partition determined may be identified as most likely to represent the same sound as the current partition. This comparison process may be performed iteratively, for individual ones of the remaining partitions. Correlation results of the comparison process may be recorded to represent the partition time period of the correlated partition as a function of partition time period of the current partition.