Dynamic Audio Stream Mixing for Gapless Customized Playback
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Solution Overview
Problem
Existing digital audio content delivery systems suffer from playback gaps and inartful characteristics, undermining the listening experience and putting terrestrial radio stations at a competitive disadvantage due to reliance on outdated human-driven processes and lack of automated customization.
Innovation Solution
A system and method for analyzing audio files to determine attributes, identifying eligible and ineligible portions for mixing, and generating instructions for seamless audio data streaming, using a multidimensional database to sequence and mix audio files based on user inputs and deep features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated audio analysis and dynamic mixing systems are implemented, then audio content delivery quality and customization are improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent segments audio content into discrete portions with identified start and end times, creating manageable units that can be independently analyzed and mixed. This segmentation allows the complex task of audio delivery to be broken down into smaller, more manageable operations while maintaining high quality through precise control of each segment.
Solution Approach 2:
The system performs preliminary analysis of audio files to identify eligible portions, their characteristics, and optimal mixing parameters before actual playback. This advance preparation includes tagging audio segments with metadata about their suitability for mixing, which simplifies real-time processing and reduces system complexity during actual content delivery.
2Productivity
If automated audio analysis and dynamic mixing are implemented, then productivity and customization capability are improved, but computational resources and processing time increase
Solution Approach 1:
The system performs audio analysis, eligibility determination, and mixing parameter optimization in advance during off-peak periods or during initial content ingestion. This preliminary processing creates pre-computed metadata and mixing instructions that can be rapidly applied during actual content delivery, significantly improving productivity while spreading computational resource usage over time.
Solution Approach 2:
The automated system analyzes audio files, determines mixing eligibility, and generates mixing instructions without requiring manual intervention. This self-service capability increases productivity by eliminating human labor bottlenecks while the system manages its own computational resources through efficient algorithms and caching strategies.
3Device complexity
If traditional human-driven audio processing is used, then system simplicity is maintained, but audio content quality and consistency deteriorate due to human error and manual processes
Solution Approach 1:
The patent replaces manual human-driven audio processing with automated computer-based analysis and mixing systems. This substitution eliminates human error and inconsistency while maintaining reasonable system complexity through software-based solutions. The automated system consistently applies mixing rules and quality standards without fatigue or variation.
Solution Approach 2:
The system automatically analyzes audio files, determines eligibility for mixing, identifies optimal portions, and generates mixing instructions without human intervention. This self-service automation ensures consistent quality application across all content while keeping the interface and basic operations relatively simple for users.
Data Source
AI summary
Disclosed are systems, servers and methods for providing a novel framework that enables the unique cataloging and organization of audio files, upon which audio rendering experiences can be created and provided to requesting users, whether the users are individuals or third-party partners. The disclosed framework enables audio files to be stripped down, uniquely stored, and then stitched together in a novel manner that previously did not exist within the computing arts. The disclosed systems and methods, therefore, provide a novel platform where audio is not just provided to consumers, but audio experiences are compiled from various types of audio formats and types in a unique, dynamically determined manner for a listening user.