Sequence Predictor for Latency Compensation in Virtual Orchestras
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Solution Overview
Problem
Current methods for generating audio in electronic games and simulations, such as using real-life orchestras or MIDI software, fail to produce realistic and emotionally resonant music, and are impractical or mechanically sounding, necessitating a technological enhancement for computer-based audio generation.
Innovation Solution
A computer-implemented method using a sequence predictor, generated by a machine learning model, to predict musical entries and synchronize audio output in real-time, allowing for the simulation of a virtual orchestra that can play in sync despite network latency, by predicting musical notes and timing based on historical data and user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-life orchestras are used to generate audio, then audio quality and emotional resonance are improved, but practicality and ease of use deteriorate
Solution Approach 1:
The patent creates virtual copies of real orchestras through digital instrument models that replicate the acoustic properties and playing characteristics of real instruments. These virtual instruments are trained on recordings from real orchestras to capture nuanced performance details, enabling high-quality audio generation without the logistical burden of assembling and managing actual orchestras.
Solution Approach 2:
The patent replaces the mechanical system of real orchestras (physical instruments, human musicians, coordination logistics) with a computational system consisting of AI models, digital signal processing, and software-based instrument simulation. This substitution maintains audio quality while dramatically improving practicality and ease of deployment.
2Ease of operation
If MIDI software is used to generate audio, then ease of operation is improved, but audio quality and emotional resonance deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where virtual musicians observe and respond to each other's performances in real-time, mimicking how real musicians interact. The conductor AI analyzes the overall ensemble performance and provides corrective feedback to individual virtual instruments, enabling expressive, emotionally resonant playing while maintaining ease of operation through automated coordination.
Solution Approach 2:
The patent implements dynamic performance characteristics where virtual instruments can vary their playing style, tempo, and expression based on contextual cues and interactions with other instruments. This dynamic behavior replaces the static, mechanical nature of traditional MIDI with adaptive, emotionally expressive performance that responds to the musical context.
3Adaptability or versatility
If distributed virtual orchestra is used, then adaptability and versatility are improved, but network latency and synchronization issues worsen
Solution Approach 1:
The system performs preliminary actions by having virtual musicians predict future musical events and prepare their responses in advance. The conductor AI anticipates upcoming cues and coordinates instrument responses before they are needed, compensating for network latency by proactively adjusting timing and synchronization to maintain reliable ensemble performance across distributed systems.
Data Source
AI summary
Sequence predictors may be used to predict one or more entries in a musical sequence. The predicted entries in the musical sequence enable a virtual musician to continue playing a musical score based on the predicted entries when the occurrence of latency causes a first computing system hosting a first virtual musician to not receive entries or timing information for entries being performed in the musical sequence by a second computing system hosting a second virtual musician. The sequence predictors may be generated using a machine learning model generation system that uses historical performances of musical scores to generate the sequence predictor. Alternatively, or in addition, earlier portions of a musical score may be used to train the model generation system to obtain a prediction model that can predict later portions of the musical score.