Sound Effect Recommendation Network for Audio-Visual Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Sound designers face challenges in selecting appropriate sound effects for silent video sequences, as they must manually search through large digital audio databases, leading to an artistic and iterative process that may result in sounds that differ significantly from reality.
Innovation Solution
The application of machine-learning techniques, specifically neural networks, to develop a Sound Effect Recommendation Tool that learns audio-visual correlations by training on large datasets of videos with mixed sound sources, allowing for the recommendation of sound effects that accurately match visual scenes without manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual search through digital audio databases is used, then sound designers can select sounds based on artistic imagination, but the process becomes time-consuming and the selected sounds may not accurately reflect physical reality
Solution Approach 1:
The patent replaces the manual mechanical search process through audio databases with an automated machine learning system. The neural network automatically analyzes visual scenes and retrieves appropriate sound effects, eliminating the need for sound designers to manually search through large audio libraries while maintaining accurate representation of physical reality through AI-driven audio-visual correlation learning
2Adaptability or versatility
If manual sound selection is used, then sound designers have creative freedom, but accuracy in matching physical sound properties deteriorates
Solution Approach 1:
The system enables self-service by allowing the neural network to automatically analyze visual scenes and select appropriate sound effects without requiring sound designers to manually search and evaluate audio options. The model learns audio-visual correlations from training data, automatically matching sounds to their physical contexts while preserving creative flexibility through customizable retrieval parameters
3Productivity
If automated sound effect recommendation is implemented, then time efficiency is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary neural network model that mediates between visual input and audio output. This intermediary system, trained on audio-visual correlations, automatically bridges the gap between visual scene analysis and sound effect selection, improving productivity while managing complexity through a specialized intermediate layer rather than requiring direct complex integration
Data Source
AI summary
A Sound effect recommendation network is trained using a machine learning algorithm with a reference image, a positive audio embedding and a negative audio embedding as inputs to train a visual-to-audio correlation neural network to output a smaller distance between the positive audio embedding and the reference image than the negative audio embedding and the reference image. The visual-to-audio correlation neural network is trained to identify one or more visual elements in the reference image and map the one or more visual elements to one or more sound categories or subcategories within an audio database.