Blind Binaural Detection via ML Feature Extraction
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Solution Overview
Problem
Current binaural audio technologies face challenges in detecting when upstream content has been binauralized, leading to potential adverse user experiences due to compounded magnitude and phase responses from dual processing in source devices and headphones, especially in wireless applications like Bluetooth audio, where communication profiles for such information are lacking.
Innovation Solution
A blind detection process using machine learning techniques, comprising a feature extractor, classifier, virtualizer, and mixer, which extracts features like inter-channel time and phase differences, and applies machine learning models to determine if an audio signal is binaural or stereo, thereby preventing unnecessary binaural processing and ensuring audio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If binaural processing is applied in both source device and headphone, then audio processing capability is enhanced, but audio quality deteriorates due to compounded magnitude and phase responses
Solution Approach 1:
The patent applies preliminary action by performing blind detection of binauralization status on the input audio signal before applying binaural processing. The system extracts features from the audio signal, classifies whether it is already binauralized, and makes a decision to enable or disable the binaural processing stage accordingly, preventing double processing before it occurs
Solution Approach 2:
The patent implements feedback by using the output of the classification process (detection result) to control the binaural processing stage. The system continuously monitors the audio signal characteristics, determines binauralization status, and adjusts the processing pipeline based on this feedback, creating a closed-loop control system that prevents audio quality degradation
2Ease of operation
If communication profile is added to signal binauralization status, then processing control is improved, but device complexity increases
Solution Approach 1:
The patent applies self-service by implementing blind detection that operates autonomously without requiring external communication or metadata exchange. The system extracts features directly from the audio signal itself, performs classification, and controls processing based on this self-generated information, eliminating the need for additional communication profiles or metadata channels
Solution Approach 2:
The patent extracts the essential information needed for binauralization detection directly from the audio signal through feature extraction processes. By taking out and analyzing specific acoustic features (such as inter-channel time differences, inter-channel level differences, and spectral characteristics), the system obtains sufficient information to make processing decisions without requiring external communication infrastructure
3Measurement precision
If blind detection using machine learning is implemented, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the detection process into distinct stages: feature extraction, classification, and decision-making. The feature extraction stage breaks down the audio signal into meaningful acoustic characteristics, which are then processed by the classification stage. This segmentation allows the system to manage computational complexity by processing information in manageable stages rather than as a monolithic complex operation
Solution Approach 2:
The patent applies partial action by extracting only the most relevant features from the audio signal that are necessary for binauralization detection. Rather than analyzing all possible signal characteristics, the system focuses on key features such as inter-channel time differences, inter-channel level differences, and specific spectral properties, achieving sufficient detection accuracy with reduced computational effort
Data Source
AI summary
An apparatus and method of blind detection of binauralized audio. If the input content is detected as binaural, a second binauralization may be avoided. In this manner, the user experience avoids audio artifacts introduced by multiple binauralizations.