Adaptive Audio Mixing via Neural Network Stem Selection
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Solution Overview
Problem
Commercial video games rely on pre-recorded audio tracks that provide a predictable and repetitive audio experience, failing to adapt to dynamic game scenarios and user interactions.
Innovation Solution
A neural network-based adaptive audio mixing system that selects and mixes pre-recorded music stems in real-time based on game scene characteristics, user interactions, and emotional responses, using reinforcement learning to adjust parameters and create a unique, non-deterministic audio mix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-recorded audio tracks are used, then audio quality is maintained, but audio experience becomes repetitive and predictable
Solution Approach 1:
The system transitions from static pre-recorded audio tracks to dynamic real-time audio mixing. A trained neural network dynamically selects and mixes audio stems based on real-time game state parameters, player actions, and contextual factors, creating continuously varying audio experiences while maintaining professional quality through controlled mixing of pre-recorded elements.
Solution Approach 2:
The audio track is segmented into separate stems (instrumental layers, melodic elements, rhythmic components) that can be independently selected and mixed. This segmentation allows the system to recombine these elements in numerous variations, preventing repetition while maintaining the quality of individual components.
2Adaptability or versatility
If real-time adaptive mixing is implemented, then audio experience becomes dynamic and engaging, but system complexity increases
Solution Approach 1:
Audio stems are pre-recorded and pre-processed into compatible segments during production. This preliminary action allows the runtime system to focus only on selection and mixing parameters rather than generating audio from scratch, significantly reducing computational complexity while maintaining adaptability.
Solution Approach 2:
Instead of requiring complex real-time audio synthesis capabilities, the system uses pre-recorded stems as templates or copies of professional audio material. The neural network learns to mix these copies based on game state, avoiding the need for complex generative audio models while achieving dynamic adaptability.
3Adaptability or versatility
If multiple audio stems are mixed in real-time, then audio variety increases, but processing time and computational resources increase
Solution Approach 1:
The system controls the number of audio stems mixed at any given time by adjusting mixing parameters such as stem selection, volume levels, panning positions, and crossfade durations. The neural network learns optimal parameter combinations for different game states, achieving variety through parameter variation rather than processing excessive numbers of stems simultaneously.
4Productivity
If pre-recorded tracks are used, then production efficiency is maintained, but user engagement decreases due to repetition
Solution Approach 1:
The system automatically mixes and adapts audio content based on game state without requiring manual composition for each scenario. The trained neural network serves itself by learning from training data and making autonomous mixing decisions, maintaining production efficiency while dramatically improving adaptability and user engagement through dynamic audio variation.
Data Source
AI summary
Systems, apparatuses, and methods for performing adaptive audio mixing are disclosed. A trained neural network dynamically selects and mixes pre-recorded, human-composed music stems that are composed as mutually compatible sets. Stem and track selection, volume mixing, filtering, dynamic compression, acoustical/reverberant characteristics, segues, tempo, beat-matching and crossfading parameters generated by the neural network are inferred from the game scene characteristics and other dynamically changing factors. The trained neural network selects an artist's pre-recorded stems and mixes the stems in real-time in unique ways to dynamically adjust and modify background music based on factors such as game scenario, the unique storyline of the player, scene elements, the player's profile, interest, and performance, adjustments made to game controls (e.g., music volume), number of viewers, received comments, player's popularity, player's native language, player's presence, and/or other factors. The trained neural network creates unique music that dynamically varies according to real-time circumstances.