Sparse Sound Field Decomposition for Wavefield Synthesis Coding
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Solution Overview
Problem
Existing wavefield synthesis coding methods face high computation costs due to the need to process all sound field information and perform matrix computations across all grid points in the analysis space, especially when extracting point sound sources using sparse decomposition.
Innovation Solution
A coding apparatus and method that estimates sound source presence at a coarser granularity, limiting the sparse sound field decomposition to areas where sound sources are likely present, reducing the computation burden by focusing the analysis on specific areas rather than the entire grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sparse sound field decomposition is performed using all grid points in the analysis space, then the point sound source extraction accuracy is improved, but the computation amount becomes huge
Solution Approach 1:
The analysis space is divided into multiple local regions, each centered at a grid point. The decomposition is performed locally in each region rather than globally across all grid points, reducing the computational burden while maintaining accuracy for identifying point sound sources in each local area.
Solution Approach 2:
Different processing approaches are applied to different regions: local sparse decomposition is applied where point sound sources are likely present, while other regions use different processing. This allows high precision where needed while reducing overall computation by avoiding exhaustive processing everywhere.
2Measurement precision
If wavefield synthesis coding processes all sound field information, then the sound field reconstruction quality is improved, but the computation amount becomes huge
Solution Approach 1:
Point sound source components are extracted from the sound field using local sparse decomposition, separating them from the ambient sound field. This allows the coding system to process only the extracted point sound sources with high precision while handling the remaining ambient field more efficiently, reducing overall computation.
Solution Approach 2:
Instead of performing full sound field decomposition across all grid points, the method applies local sparse decomposition only in specific local regions where point sound sources are likely present. This partial action approach achieves sufficient reconstruction quality for point sources without the excessive computation of global decomposition.
Data Source
AI summary
A sound source estimation unit (101) estimates, in a space as a target of sparse sound field decomposition, an area where a sound source is present at second granularity that is coarser than first granularity of a position where a sound source is assumed to be present in the sparse sound field decomposition. A sparse sound field decomposition unit (102) decomposes an acoustic signal observed by a microphone array into a sound source signal and an ambient noise signal by performing a sparse sound field decomposition process at the first granularity for the acoustic signal in the area at the second granularity where the sound source is estimated to be present in the space.