Audio Channel Classification for Correct Multichannel Playback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional manual analysis of audio channels in multichannel audio files is labor-intensive, time-consuming, inconsistent, and inaccurate, making it inefficient to ensure correct playback on speaker systems.
Innovation Solution
Employing machine-learning techniques to automatically classify and characterize audio channels in audio data, determining their types and order using audio channel representations and training data, thereby generating metadata for correct playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to identify audio channel types, then accuracy can be maintained through human expertise, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the mechanical human analysis process with an automated machine learning system that uses audio channel representations and training data to classify channel types. This substitution eliminates manual labor while maintaining consistent and accurate identification across all audio files.
Solution Approach 2:
The system enables audio files to self-identify their channel types through automated machine learning classification. The audio channel representations automatically analyze and characterize the audio data without requiring external human intervention, making the process efficient and scalable.
2Reliability
If manual analysis is used to characterize audio content, then detailed examination can be performed, but the process becomes inconsistent and inefficient
Solution Approach 1:
The patent transforms the analysis process by changing from subjective human parameters to objective machine learning parameters. The system uses consistent mathematical and statistical methods to analyze audio channel representations, ensuring identical processing standards are applied to all audio files regardless of when or by whom they are analyzed.
Solution Approach 2:
By replacing human analysts with an automated machine learning system, the patent eliminates variability in human judgment and performance. The system provides consistent, repeatable results across different audio files and different processing instances, while significantly reducing the time required for analysis.
3Reliability
If traditional methods are used to ensure correct audio playback, then speaker channel assignment can be verified, but the process requires significant human resources
Solution Approach 1:
The system automatically generates metadata that identifies audio channel types and enables correct speaker assignment without requiring human verification. The machine learning model self-characterizes the audio content and provides actionable metadata that can be directly used by playback systems to ensure correct channel-to-speaker mapping.
Solution Approach 2:
The patent introduces an intermediary machine learning system that bridges the gap between raw audio data and correct playback configuration. This intermediary automatically analyzes audio channel representations and generates metadata that serves as a reliable guide for speaker assignment, eliminating the need for complex manual verification processes.
Data Source
AI summary
The current embodiments relate to an audio processing system that may determine the identity or type of audio channel of audio channels present in audio data. For instance, the audio processing system may include one or more processors that receive audio data that includes a plurality of audio channels, determine a respective type of audio channel for each respective audio channel of the plurality of audio channels, and generate characterized audio data indicative of the respective type of audio channel for each respective audio channel of the plurality of audio channels.