Semantic Video-Audio Alignment for Background Audio Construction

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Solution Overview

Problem

Existing video background music selection methods lack efficiency and correlation with video content, resulting in low matching accuracy and display effectiveness.

Innovation Solution

Perform semantic segmentation on video data to generate a semantic segmentation map, extract audio features from an audio set, and align these features to select a target audio file for constructing background audio that matches the video content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If background music is selected from a pre-established library based on video theme, then the process is simple, but the matching accuracy with video content is low

Engineering Contradiction:

Improvesimplicity of background music selection processVSAvoidmatching accuracy between background music and video content

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent segments video content into multiple dimensions including semantic segmentation (foreground/background objects), spatial segmentation (position information), and temporal segmentation (time information). This multi-dimensional segmentation enables precise matching between video content and background music characteristics, resolving the contradiction by maintaining simplicity through automated feature extraction while significantly improving matching accuracy through comprehensive content analysis.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms video content into multiple feature parameters including semantic features, spatial features, and temporal features. By changing the representation parameters from simple theme tags to comprehensive multi-dimensional features, the system achieves high matching accuracy while keeping the selection process simple through automated parameter comparison and scoring.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If comprehensive video content analysis is performed to improve matching accuracy, then the matching precision improves, but the processing complexity increases

Engineering Contradiction:

Improvematching accuracy between background music and video contentVSAvoidcomplexity of video processing system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent divides the complex video analysis task into independent segmentation modules: semantic segmentation network, spatial feature extraction, and temporal feature extraction. Each module handles a specific aspect of video content, reducing overall system complexity while achieving comprehensive analysis through modular design and parallel processing.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal feature extraction framework that simultaneously extracts semantic, spatial, and temporal features using multi-functional processing modules. This universal approach improves matching accuracy by analyzing multiple video dimensions while managing complexity through integrated processing that handles all feature types within a unified system architecture.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If traditional background music selection methods are used, then the process is fast, but the correlation with video content is poor

Engineering Contradiction:

Improvespeed of background music selectionVSAvoidcorrelation between background music and video content

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent performs preliminary extraction of video features (semantic, spatial, temporal) and audio features before the actual matching process. This preliminary action enables rapid comparison and scoring during music selection, maintaining high speed while ensuring strong correlation through pre-computed comprehensive video content representation that captures essential visual and temporal characteristics.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional manual or simple keyword-based music selection with an automated neural network-based feature extraction and matching system. This substitution maintains productivity through automated processing while dramatically improving reliability by using deep learning models to accurately capture video content characteristics and correlate them with appropriate background music.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12444191B2Background audio construction

Publication Date: 2025.10.14 SHANGHAI HODE INFORMATION TECH CO LTD

AI summary

A background audio construction method is provided. The background audio construction method includes: performing semantic segmentation on to-be-processed video data to generate a corresponding semantic segmentation map, and extracting a semantic segmentation feature of the to-be-processed video data based on the semantic segmentation map; extracting an audio feature of each audio file in a pre-established audio set; and aligning the audio feature and the semantic segmentation feature, selecting a target audio file from the audio set based on an alignment result, and constructing background audio for the to-be-processed video data based on the target audio file.