Context-Aware Background Audio Generation for Video Games
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Solution Overview
Problem
Existing video game background audio methods produce repetitive and unrealistic conversational dialogue, breaking player immersion.
Innovation Solution
A system utilizing machine learning models processes text and contextual data to generate dynamic and realistic background audio, including conversational dialogue and sound effects, tailored to the game environment and state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to generate background audio with conversational dialogue, then the implementation is simple, but the audio becomes repetitive and unrealistic, breaking player immersion
Solution Approach 1:
The patent introduces an intermediary system comprising multiple specialized modules (dialogue generation module, audio synthesis module, context analysis module) that mediate between the simple input requirements and the complex output requirements. These intermediary components process and transform data step-by-step to generate realistic background audio without requiring the entire system to be maximally complex at once.
Solution Approach 2:
The background audio generation system is designed with multi-functional capabilities that allow it to handle various types of conversational dialogue, multiple character voices, different environmental contexts, and various audio effects within a single unified system. This universality enables the system to maintain realism across diverse scenarios without requiring separate specialized systems for each function.
2Adaptability or versatility
If short and repetitive dialogue lines are used in background audio, then the implementation is straightforward, but player immersion is broken due to unrealistic conversations
Solution Approach 1:
The dialogue generation module employs dynamic characteristics by using context analysis to adapt conversation content, length, and style based on the current game state and environmental context. This allows the system to generate varied and realistic conversations that respond to different situations, maintaining high adaptability while the automated context analysis preserves generation efficiency.
Solution Approach 2:
The system performs preliminary context analysis and dialogue planning before generating the actual audio content. By pre-processing the contextual information and preparing dialogue structures in advance, the system can efficiently generate varied conversations without sacrificing productivity during the actual audio generation phase.
3Reliability
If machine learning models are used to process text and contextual data, then realistic and contextually relevant background audio is generated, but the computational resources and processing time increase
Solution Approach 1:
The machine learning processing is segmented into multiple specialized modules that handle different aspects of audio generation independently (dialogue generation, audio synthesis, context analysis). This segmentation allows for optimized processing of each function and enables parallel execution where possible, reducing overall processing time while maintaining contextual relevance.
Solution Approach 2:
The system performs preliminary processing of contextual data and text input before the main audio generation process. By pre-analyzing context and preparing data structures in advance, the machine learning models receive pre-processed input that requires less computational time during the actual audio synthesis phase, thereby reducing overall processing time while maintaining high contextual relevance.
Data Source
AI summary
This specification describes a method for generating background audio in a video game. The method is implemented by one or more processors and the method comprises: obtaining, by one or more of the processors, text data comprising text for speech audio that is to be present in the background audio; obtaining, by one or more of the processors, contextual data comprising data descriptive of an environment in the video game; and generating, by one or more of the processors, the background audio based upon processing the text data and the contextual data using one or more machine learning models.