Audio Cue Matching for Accurate TV Content Recognition
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Solution Overview
Problem
Television devices struggle to accurately identify the content being presented due to increased channel capacity and diverse programming sources, hindering the execution of enhanced functions like interactive television and contextual browsing.
Innovation Solution
Implementing machine-learning processes to isolate and process audio channels for content recognition, using speech-to-text models and machine-learning models to generate and compare audio cues with known media databases for identification, with media devices or servers processing or transmitting audio cues for recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If television devices provide hundreds of channels and thousands of on-demand programming sources, then channel capacity and programming diversity increase, but the ability to detect and identify particular programming decreases
Solution Approach 1:
The patent extracts audio cues from the audio channel of media segments and uses these extracted cues for content identification. By isolating and processing specific audio features rather than analyzing the entire media stream, the system can accurately identify content across hundreds of channels and thousands of programming sources.
Solution Approach 2:
The patent introduces audio cues as an intermediary element between the media content and the identification process. These audio cues serve as mediators that bridge the gap between diverse programming sources and the television device's ability to detect and identify particular programming, enabling accurate content recognition across multiple channels.
2Ease of operation
If televisions provide functions like interactive television and contextual browsing, then user experience is enhanced, but the system requires accurate content detection which is currently prevented
Solution Approach 1:
The patent replaces traditional content detection methods with audio-based cue processing. By substituting conventional detection mechanisms with audio analysis and machine learning models, the system achieves reliable content identification that enables enhanced functions like interactive television and contextual browsing.
Solution Approach 2:
The television device performs self-identification of content by processing audio cues from the media segments it receives. This self-service capability allows the device to automatically detect and identify programming without external intervention, enabling reliable execution of enhanced functions.
3Measurement precision
If audio cues are processed using machine-learning models, then content identification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments the audio channel into discrete audio cues that can be processed by machine-learning models. By dividing the audio stream into identifiable segments or features, the system achieves high content identification accuracy while managing processing complexity through structured data organization.
Data Source
AI summary
An automated content recognition system can identify media presented by a media device using an audio channel of the media. The media device may isolate an audio segment from the audio channel of the media. A speech-to-text model may be executed using the audio segment to identify a sequence of words represented by the audio segment. The media device may transmit an unknown audio including the sequence of words to an ACR server. The ACR server may compare the sequence of words of the unknown audio cue to words of reference audio cues associated with known media segments. Upon identifying a reference audio cue that matches the unknown audio cue, the ACR server may cause the media device to execute one or more commands based on an identifier of the matching reference audio cue