Audio Burst Detection for Real-Time Sports Highlight Boundaries
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Solution Overview
Problem
Existing television systems lack the capability to automatically generate metadata and highlights for sporting events in real-time, particularly for identifying and tracking short bursts of high-energy audio events such as tennis ball hits, which are crucial for enhancing interactive television applications.
Innovation Solution
A system that performs initial audio signal analysis in the time domain to detect short bursts of high-energy audio, constructs a spectrogram, and applies a 2-D diamond-shaped time-frequency area filter to extract pronounced spectral magnitude peaks, followed by a qualifier computation and filtering to refine the audio event timeline, which is then appended to metadata for generating highlights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional television systems are used, then system simplicity is maintained, but the capability to automatically detect and generate metadata for sporting events in real-time is lacking
Solution Approach 1:
The audio processing is divided into distinct stages: time-domain analysis for initial event detection, frequency-domain analysis for spectral peak extraction, and qualifier computation for refinement. Each stage processes specific features and passes results to the next stage, enabling automated metadata generation through modular, manageable processing steps
Solution Approach 2:
The system performs preliminary audio signal analysis by constructing spectrograms and identifying potential audio events before final metadata generation. Time-domain analysis with energy detection and frequency-domain analysis with spectral peak extraction are conducted in advance to prepare refined event timelines for metadata creation
2Measurement precision
If comprehensive audio analysis is performed to detect short bursts of high-energy audio events, then detection precision is improved, but processing time increases
Solution Approach 1:
The system applies periodic analysis windows to the audio signal in both time and frequency domains. By using overlapping windows with specific hop sizes and analyzing spectral peaks within defined frequency ranges, the system achieves comprehensive detection while maintaining real-time processing capability through structured, periodic analysis cycles
Solution Approach 2:
The system transitions from one-dimensional time-domain analysis to two-dimensional time-frequency domain analysis by constructing spectrograms. This dimensional expansion enables simultaneous detection of temporal patterns and spectral characteristics, improving detection precision for short bursts of high-energy audio events without proportionally increasing processing time
3Measurement precision
If spectral analysis with 2-D diamond-shaped time-frequency area filter is applied, then audio event identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The 2-D diamond-shaped time-frequency area filter applies different analysis weights to different regions of the spectrogram. By focusing computational resources on local areas around detected spectral peaks and using shape parameters to control the filter's spatial extent, the system achieves high identification accuracy while managing computational complexity through localized rather than global processing
Solution Approach 2:
The filter's shape parameters are dynamically adjusted based on the characteristics of the audio signal and detected events. The diamond-shaped filter adapts its size and orientation in the time-frequency plane to match the local structure of spectral peaks, enabling accurate event identification with optimized computational requirements for different signal conditions
Data Source
AI summary
A boundary of a highlight of audiovisual content depicting an event is identified. The audiovisual content may be a broadcast, such as a television broadcast of a sporting event. The highlight may be a segment of the audiovisual content deemed to be of particular interest. Audio data for the audiovisual content is stored, and the audio data is automatically analyzed to detect one or more audio events indicative of one or more occurrences to be included in the highlight. Each audio event may be a brief, high-energy audio burst such as the sound made by a tennis serve. A time index within the audiovisual content, before or after the audio event, may be designated as the boundary, which may be the beginning or end of the highlight.