AI-Guided Live Event Audio Mixing via Mobile Device
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Solution Overview
Problem
Attendees of live events face challenges in determining the best audio channel or mix in real-time without user input, as they often rely on their mobile devices to stream audio over wireless networks.
Innovation Solution
A computerized method and system for AI/ML-guided live event audio mixing, where a mobile computing device receives and processes a live audio signal, automatically switching between audio mixes based on duration since the event began and an AI/ML algorithm trained on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated switching between audio mixes is implemented, then user convenience and audio quality are improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing the audio signal to extract features and pre-training the machine learning model with historical audio data before the live event begins. This allows the automated mix switching to function effectively during the event without requiring real-time complex decision-making, thereby improving user convenience while managing system complexity.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the raw audio signal and the audio mixing decisions. This intermediary component analyzes audio features and determines optimal mix selections, simplifying the overall system architecture while enabling automated switching functionality.
2Reliability
If machine learning algorithms are used for real-time mix selection, then audio quality is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the audio processing task into distinct stages: feature extraction from the audio signal, machine learning model inference for mix selection, and audio mixing execution. This segmentation allows each component to be optimized independently, with feature extraction happening in real-time and the ML model providing predictions based on extracted features, thereby maintaining audio quality while managing processing time.
Solution Approach 2:
The system applies partial action by using a simplified machine learning model that processes only the most relevant audio features rather than analyzing the complete audio signal in full detail. This approach provides sufficient accuracy for mix selection while significantly reducing computational requirements and processing time.
Data Source
AI summary
A method for AI/ML-guided live event audio mixing includes receiving a data representation of a live audio signal corresponding to a live event via a wireless network. The method also includes processing the data representation of the live audio signal corresponding to the live event into a live audio stream having audio mixes. The method also includes initiating playback of the live audio stream based on a first mix via a headphone communicatively coupled to a mobile computing device at the live event. The method also includes determining a second mix based on a duration since a beginning of the live event and an AI/ML algorithm. The method also includes initiating playback of the live audio stream based on the determined second mix via the headphone communicatively coupled to the mobile computing device at the live event.