Adaptive Spatialization Model Selection for Audio Reconstruction
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Solution Overview
Problem
Existing methods for reconstructing multi-channel audio data from defective spatialization data often result in abrupt sound source displacements, leading to disruptive listening experiences, particularly in binaural signals, as they rely on a single prediction model that is unsatisfactory for spatialization data errors.
Innovation Solution
A method that tests the validity of spatialization data and selects a prediction model from a plurality of models based on the received data, using predicted spatialization values to reconstruct multi-channel audio data adaptively, thereby alleviating defects more effectively than prior art.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single prediction model is used to replace defective spatialization data, then the reconstruction process is simple, but abrupt sound source displacements occur leading to disruptive listening experiences
Solution Approach 1:
The system dynamically selects between multiple prediction models based on the characteristics of the received spatialization data. Instead of using a static single model, the system adapts the prediction approach by choosing from several models (e.g., different interpolation methods or prediction algorithms) depending on the data conditions, thereby improving concealment quality while maintaining operational feasibility through automated model selection
Solution Approach 2:
The system changes the parameter of prediction model selection based on the characteristics of the spatialization data. By monitoring data validity and characteristics, the system adjusts which prediction model is applied, transforming the fixed parameter of a single model into a variable parameter that adapts to different data conditions, thus resolving the contradiction between simplicity and quality
2Power
If defective spatialization values are replaced with arbitrary values, then the reconstruction is computationally simple, but the listener experiences disruptive abrupt returns to mono-channel sound
Solution Approach 1:
The system performs preliminary actions by pre-establishing multiple prediction models and selecting the appropriate one before actual data reconstruction is needed. When defective spatialization data is detected, the system has already prepared multiple prediction approaches and can immediately apply the most suitable one, avoiding both computational waste and abrupt sound displacements
Solution Approach 2:
The system creates multiple prediction models as alternative copies of the prediction function. Instead of relying on a single prediction mechanism, the system maintains several model copies (e.g., different interpolation strategies) and selects the appropriate copy based on data characteristics, thereby providing robust concealment without excessive computational burden
Data Source
AI summary
A method for processing sound data is provided for the reconstruction of multi-channel audio data on the basis at least of data on a reduced number of channels and of spatialization data. A test is carried out to determine whether the spatialization data received are valid. If the test is positive, a spatialization value is predicted according to a per respective model of a plurality of models. A prediction model is chosen on the basis of the spatialization values thus predicted and on the basis of the spatialization data received, to permit, in case of subsequent reception of defective spatialization data, a prediction according to this chosen model of a spatialization value and to use this predicted spatialization value for the reconstruction of the multi-channel audio data.