Distributed AI Smell Generation for Low-Latency Audiovisual Sync
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Solution Overview
Problem
Current entertainment systems struggle to accurately control and transmit aroma in real-time with audiovisual data due to high computing demands, leading to slow processing speeds and heavy loads on local computers, and existing audio classification technologies are inefficient for large data sets.
Innovation Solution
A distributed processing system utilizing a cloud server and base stations with AI models to analyze audiovisual data, distributing the processing load and utilizing cloud resources for high-speed, low-latency smell classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If audio classification is processed on a local computer, then the system can operate independently, but the processing speed is slow and computing load is heavy
Solution Approach 1:
The system divides the audio classification task into two segments: local preprocessing (audio extraction, normalization, feature extraction) and cloud-based classification. The local computer handles data preparation while the cloud server performs the computationally intensive classification, thereby reducing local computing load and improving overall processing speed.
Solution Approach 2:
The patent introduces a cloud server as an intermediary between the local computer and the final classification output. The cloud server receives audio features from the local computer, performs the classification using powerful cloud resources, and returns the results. This intermediary approach transfers the heavy computing burden from the local device to the cloud.
2Reliability
If real-time audio classification is implemented, then olfactory experience can be synchronized with audiovisual content, but the processing latency increases
Solution Approach 1:
The system performs preliminary actions locally including audio extraction, normalization, and feature extraction before sending data to the cloud server. By completing these preparatory steps locally, the patent minimizes the data transmission size and processing time required in the cloud, thereby reducing overall latency while maintaining synchronization accuracy.
3Productivity
If distributed processing is used, then computing efficiency improves and latency reduces, but system complexity increases
Solution Approach 1:
The patent implements a simplified distributed architecture where the cloud server provides standardized classification services that can be copied and accessed by multiple local devices. The cloud server maintains a repository of audio-classification mappings that can be queried by different local computers, allowing the system to handle multiple users and devices without proportionally increasing complexity.
Data Source
AI summary
A method for converting audiovisual data into olfactory experience based on distributed processing, uses distributed processing to realize high-speed and low-latency audio and images classification. The system includes a data center, a cloud server, a data set, media content, and a base station, gaming PCs, headphones and smell generators which can be widely used in games, movies, media and other scenes. The artificial intelligence is used to identify audio or images in the scene to obtain corresponding smell feature data. For example, the sound of gunfire or the picture of a gun being fired in a movie scene can be analyzed and obtain gunpowder smell feature data. And then the smell generating unit mixes the corresponding aromatic compounds based on the above gunpowder smell feature data obtained through artificial intelligence analysis and finally releases the corresponding smell, to allow users to feel an olfactory experience in addition to vision and hearing, thereby achieving a better live experience.