Media Classification for Audio Reproduction Settings
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Solution Overview
Problem
Electronic devices often incorrectly upmix stereo music to 5.1 channels or other surround sound formats, losing artistic intent and introducing spatial and timbre artifacts, and fail to provide optimal audio settings for different media classes, such as movies, music, voice, and news, leading to suboptimal media presentation.
Innovation Solution
A method using machine learning models to classify media into classes like movie, music, voice, advertisement, and sports based on text and numerical metadata, allowing for the automatic selection of appropriate audio reproduction settings, such as spatial filtering for 3D audio or equalization for music, to preserve artistic intent and enhance audio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If stereo music is automatically upmixed to 5.1 channels or surround sound formats, then audio spatial coverage is improved, but artistic intent is lost and spatial/timbre artifacts are introduced
Solution Approach 1:
The system dynamically adjusts audio processing based on real-time classification of media content. Different audio reproduction settings are applied depending on the detected media class, allowing the system to adapt between stereo preservation and surround sound expansion on a per-content basis rather than using a fixed processing mode
Solution Approach 2:
The system changes audio reproduction parameters (such as channel configuration, spatial filtering, equalization settings) based on the classified media type. By modifying these parameters according to content classification, the system optimizes audio output for each media class while avoiding inappropriate processing that would degrade quality
2Device complexity
If a single audio reproduction setting is used for all media types, then device complexity is reduced, but media presentation quality deteriorates
Solution Approach 1:
The system segments media content into distinct classes (music, movies, voice, news, sports, advertisements) and applies different audio reproduction settings to each segment. This segmentation allows optimized processing for each media type without requiring manual user configuration for every piece of content
Solution Approach 2:
The system automatically classifies media content and selects appropriate audio reproduction settings without requiring user intervention. The self-service classification mechanism enables the system to autonomously optimize audio output for different media types, eliminating the need for complex manual configuration while maintaining high presentation quality
3Reliability
If manual selection of audio settings is required for each media type, then audio quality is optimized, but user operation complexity increases
Solution Approach 1:
The system performs automatic media classification and audio setting selection without requiring user input. The classification algorithm autonomously identifies media type and applies appropriate reproduction settings, making the system self-sufficient in optimizing audio quality while keeping the user interface simple
Data Source
AI summary
Examples of methods for media classification are described herein. In some examples, a method includes analyzing text associated with media using a first machine learning model to produce a first result. In some examples, the method includes analyzing numerical metadata associated with the media using a second machine learning model to produce a second result. In some examples, the method includes inputting the first result and the second result to a third machine learning model to determine a classification of the media.