Multimedia Opening Scene Detection via Audio-Visual Feature Analysis

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Solution Overview

Problem

Conventional systems for detecting opening songs in multimedia files rely on manual review and tagging, which is cumbersome, expensive, and subjective, lacking precision in identifying the exact beginning and end of the opening song.

Innovation Solution

The system automatically detects and classifies opening scenes in multimedia files by segmenting the content into scenes, extracting features, and using machine learning models to analyze these features and determine the probability that each scene is part of the opening song.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual review and tagging methods are used to detect opening songs, then the process can be performed with simple systems, but the time consumption and cost increase significantly

Engineering Contradiction:

Improveprocessing speedVSAvoidtime for manual review

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical review processes with automated audio and visual analysis systems. The system uses audio feature extraction (spectral analysis, temporal patterns) and visual feature extraction (frame analysis, scene detection) to automatically identify opening songs, eliminating the need for manual time-consuming review while significantly improving processing productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service detection by automatically analyzing multimedia content without human intervention. The automated classification algorithm processes audio and visual features independently, generating opening song detection results without requiring manual tagging or review, thereby reducing both time loss and operational costs

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual tagging is used for opening song detection, then the system complexity remains low, but the precision and consistency of detection deteriorate

Engineering Contradiction:

Improvedetection precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the opening song detection task into distinct analytical components: audio feature extraction (spectral characteristics, temporal patterns, pitch contours), visual feature extraction (frame analysis, scene transitions, text overlay detection), and classification integration. This segmentation enables precise measurement of opening song boundaries while managing system complexity through modular processing stages

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediary feature extraction layers that bridge raw multimedia data and final classification decisions. Audio features (spectral centroids, zero-crossing rates) and visual features (frame difference metrics, scene change detection) serve as intermediaries that translate complex multimedia content into quantifiable parameters, improving detection precision while maintaining manageable system complexity through standardized feature representations

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If automated detection systems are implemented, then processing productivity increases, but the computational resources and system complexity increase

Engineering Contradiction:

Improveautomated processing speedVSAvoidcomputational complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent applies partial action by focusing computational resources on key discriminative features rather than analyzing all possible audio and visual parameters. The system extracts only the most relevant audio features (spectral characteristics, temporal patterns) and visual features (scene transitions, text presence) necessary for opening song detection, achieving high productivity while controlling computational complexity through selective feature processing

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent utilizes parameter changes by transforming raw audio and visual data into standardized feature parameters suitable for classification. Audio signals are converted to spectral parameters (frequency bins, energy distribution), and visual data is transformed into scene parameters (frame difference metrics, motion vectors). These parameter transformations enable efficient automated processing while managing computational complexity through dimensionality reduction and feature standardization

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12266175B2Combining visual and audio insights to detect opening scenes in multimedia files

Publication Date: 2025.04.01 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Disclosed is a method for automatically detecting an introduction/opening song within a multimedia file. The method includes designating sequential blocks of time in the multimedia file as scene(s) and detecting certain feature(s) associated with each scene. The extracted scene feature(s) may be analyzed and used to assign a probability to each scene that the scene is part of the introduction/opening song. The probabilities may be used to classify each scene as either correlating to or not correlating to, the introduction/opening song. The temporal location of the opening song may be saved as index data associated with the multimedia file.