Audio Tempo Detection System Resolving Octave Errors
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Solution Overview
Problem
Conventional tempo estimation algorithms often inaccurately detect the prominent tempo of audio recordings due to 'octave errors' caused by certain instrumentation, leading to false detection of double or half the tempo perceived by listeners.
Innovation Solution
A method and system that identify and weigh beat rates in audio data, applying a characteristic filter such as mood or genre to determine the prominent tempo, and correct BPM estimates based on tempo classes to ensure accurate tempo classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If conventional tempo estimation algorithms analyze low-level features to detect beat rates, then beat detection capability is improved, but measurement precision deteriorates due to octave errors
Solution Approach 1:
The patent introduces high-level audio characteristics (mood, genre, instrumentation) as intermediary filters between the beat detection algorithm and the final tempo determination. These characteristics mediate the relationship by providing contextual information that helps disambiguate between multiple detected beat rates and identify the prominent tempo, thereby resolving octave errors without sacrificing beat detection capability.
Solution Approach 2:
The patent changes the parameter space from purely low-level acoustic features to a combination of low-level features and high-level characteristics. By transforming the analysis domain to include both temporal patterns and contextual metadata, the system achieves both accurate beat detection and precise tempo measurement by filtering beat rates through characteristic-based criteria.
2Adaptability or versatility
If multiple beat rates are detected from polyphonic audio, then comprehensive beat rate identification is improved, but device complexity increases
Solution Approach 1:
The patent segments the tempo determination process into distinct functional stages: beat rate detection, characteristic analysis, weighting/filtering, and prominent tempo selection. By dividing the complex task into manageable segments, each handled by specialized modules, the system achieves comprehensive beat rate identification while managing complexity through structured processing stages.
Solution Approach 2:
The patent applies partial action by detecting multiple beat rates (excessive detection) and then selectively filtering them through characteristic-based criteria to identify only the prominent tempo. This approach initially performs more analysis than strictly necessary but uses the excess information to achieve more accurate tempo determination, particularly in polyphonic contexts where multiple rhythmic layers exist.
3Measurement precision
If dynamic beat patterns are analyzed to capture tempo variations, then temporal accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-establishing characteristic profiles (mood, genre, instrumentation) that serve as filters for tempo determination. These characteristics are analyzed in advance and stored as reference data, enabling rapid comparison against detected beat rates without requiring time-consuming real-time analysis during the actual tempo determination process.
Data Source
AI summary
The prominent tempo of an audio data is determined by detecting a plurality of beat rates of the audio data. One or more audio data characteristics are used to filter through the beat rates to determine the prominent tempo. Systems, methods, and apparatuses to determine the prominent tempo are discussed herein.