Spatial error metrics of audio content
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Solution Overview
Problem
Existing audio signal processing technologies face challenges in determining spatial error metrics and audio quality degradation during format conversion, rendering, clustering, and remixing of audio objects, which can result in high bandwidth and processing requirements, and may lead to audible artifacts and inefficient representation of spatially diverse audio content.
Innovation Solution
The development of a system that computes spatial error metrics, including intra-frame and inter-frame errors, and predicts subjective audio quality using importance-weighted and normalized error metrics, which can be visualized to provide feedback for optimizing the audio object clustering process, thereby minimizing spatial errors and improving audio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio content is represented with a large number of audio objects to achieve spatial diversity and immersion, then audio quality and spatial accuracy are improved, but bandwidth and processing requirements increase
Solution Approach 1:
The patent segments audio content into individual audio objects with associated spatial metadata, allowing selective encoding and transmission of spatial information. This segmentation enables the system to maintain high spatial accuracy for important objects while reducing overall bandwidth by not transmitting redundant spatial data for all objects.
Solution Approach 2:
The patent changes the representation parameters by introducing spatial error metrics and quality degradation measures. These parameters enable adaptive encoding where spatial precision is adjusted based on content importance and channel conditions, optimizing the balance between spatial accuracy and bandwidth consumption.
2Productivity
If audio content is transformed into fewer audio objects to reduce bandwidth and processing, then transmission efficiency is improved, but spatial error and audio quality degradation occur
Solution Approach 1:
The patent implements feedback mechanisms by computing spatial error metrics and quality degradation measures during the encoding process. These metrics provide feedback that guides the clustering and merging of audio objects, allowing the system to maintain spatial accuracy within acceptable thresholds while achieving transmission efficiency through reduced object count.
Solution Approach 2:
The patent performs preliminary analysis to compute spatial error metrics before final encoding decisions are made. This preliminary action allows the system to identify critical spatial relationships that must be preserved, enabling efficient clustering that minimizes spatial error while maximizing transmission efficiency.
3Measurement precision
If spatial error metrics are computed to assess audio quality degradation, then objective quality assessment is improved, but processing complexity increases
Solution Approach 1:
The patent extracts specific spatial parameters and error metrics from the full audio signal processing chain. By focusing computation on extracted spatial metadata rather than the complete audio signal, the system achieves accurate quality assessment with reduced processing complexity compared to analyzing the full multichannel audio content.
Data Source
AI summary
Audio objects that are present in input audio content in one or more frames are determined. Output clusters that are present in output audio content in the one or more frames are also determined. Here, the audio objects in the input audio content are converted to the output clusters in the output audio content. One or more spatial error metrics are computed based at least in part on positional metadata of the audio objects and positional metadata of the output clusters.