Performance Sound Estimation Model for Remote Ensemble Latency
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Solution Overview
Problem
Existing systems for remote ensembles face challenges with sound transmission delays over communication lines, making it difficult for performers to play in concert in a natural manner.
Innovation Solution
Utilizing trained performance sound estimation models to estimate future performance sounds based on received sounds, minimizing delays by predicting sounds at actual performance times through machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sound is transmitted through a communication line for remote ensembles, then performers can perform at different places, but latency in sound transmission occurs causing delay between sound production and reception
Solution Approach 1:
The system performs preliminary actions by estimating future performance sounds before they are actually produced. The estimation circuit predicts what sound will be produced next based on current and past performance data, allowing the system to prepare and transmit estimated sounds in advance, thereby reducing the perceived latency for remote performers.
Solution Approach 2:
The system creates a copy of the performance sound through estimation. Instead of directly transmitting the actual performance sound with inherent delay, the system generates an estimated copy of what the sound will be, allowing remote performers to hear a synchronized version that compensates for transmission latency.
2Loss of time
If sound transmission latency is reduced through communication optimization, then synchronization improves, but system complexity increases
Solution Approach 1:
The estimation circuit acts as an intermediary between the actual performance sound and the transmitted sound. Instead of directly optimizing communication protocols, the system introduces this intermediate estimation layer that predicts and compensates for delays, achieving synchronization without requiring complex communication optimizations.
Solution Approach 2:
The system replaces mechanical/physical sound transmission optimization with a computational approach. Instead of focusing on improving the physical communication infrastructure, the patent substitutes a software-based estimation and prediction mechanism that calculates future sounds algorithmically, avoiding the need for complex hardware modifications.
3Loss of time
If performance sound estimation models are trained to predict future sounds, then sound reproduction delay is minimized, but computational resources and processing time increase
Solution Approach 1:
The estimation circuit performs partial estimation rather than complete prediction of all sound parameters. By focusing estimation on critical temporal aspects of the performance sound and using simplified models for less critical elements, the system achieves adequate synchronization with reduced computational overhead compared to full-spectrum sound prediction.
Data Source
AI summary
A first device is for a remote ensemble performed in a first venue and a second venue and provided in the first venue. The device includes a memory and an estimation circuit. The memory stores a performance sound estimation model. The estimation circuit inputs, into the performance sound estimation model, a performance sound obtained by a second venue device provided in the second venue to estimate an estimated future performance sound of the performance sound. The performance sound estimation model is a trained model trained to learn a sound signal corresponding to the performance sound to estimate the estimated future performance sound based on the performance sound.