Whistle Sound Analysis for Sports Video Classification
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Solution Overview
Problem
Existing methods for classifying sports video require significant processing resources, making them costly and cumbersome, as they analyze video segments and motion information, which is inefficient for identifying specific types of sporting events.
Innovation Solution
A system and method that collect and analyze whistle sound patterns in video content, using features like mel-frequency cepstrum coefficients and pitch to determine the type of sport by comparing sample whistle features with audio features from the video, and utilizing whistle occurrence data to classify the sport type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video segments and motion information are analyzed for sports classification, then classification accuracy is improved, but processing resources and system complexity increase significantly
Solution Approach 1:
The patent extracts and analyzes only the audio component (whistle sounds) from video content, separating this specific feature from the entire video stream. By focusing exclusively on whistle detection and analysis rather than processing all video segments and motion information, the system achieves sports classification with reduced computational complexity while maintaining effectiveness for whistle-based sports identification
Solution Approach 2:
The patent replaces complex visual analysis mechanisms with acoustic analysis mechanisms. Instead of using computationally intensive video segment analysis and motion detection algorithms, the system substitutes these with audio processing and whistle pattern recognition, achieving similar classification goals through a different sensory modality that requires fewer processing resources
2Measurement precision
If comprehensive video analysis is performed to identify specific sporting events, then identification accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The system extracts only the relevant audio feature (whistle sounds) from the video content, discarding unnecessary visual processing. This selective extraction of acoustic information reduces energy consumption while maintaining the ability to accurately identify sports that use whistles, as the audio signal contains sufficient discriminative information for classification
Solution Approach 2:
The patent applies partial action by performing analysis only on audio segments containing whistle sounds rather than processing the entire video stream continuously. The system detects whistle occurrences and analyzes only those temporal segments, reducing overall processing energy while maintaining identification accuracy through focused analysis of critical events
3Reliability
If detailed motion information is processed for sports classification, then classification reliability is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent substitutes visual motion analysis with acoustic whistle analysis. By replacing the mechanical complexity of video frame processing and motion vector calculation with audio signal processing and spectral analysis, the system achieves reliable sports classification through a simpler processing architecture that focuses on acoustic patterns rather than visual dynamics
Data Source
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AI summary
Disclosed are systems, methods, and computer readable media having programs for analyzing video. In one embodiment, a method includes: detecting a plurality of whistle sounds in an audio stream of a video; and determining a video content based on a plurality of properties corresponding to the plurality of whistle sounds. In one embodiment a computer readable medium having a computer program for analyzing video includes: instructions configured to generate a plurality of whistle sound patterns; instructions configured to detect a whistle sound in a video; and instructions configured to analyze the video using the whistle sound.