Audio Object Quantization Using Importance-Based Bit Allocation
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Solution Overview
Problem
As the number of audio channels increases and speaker layouts transition from 2D to 3D, the complexity of authoring and rendering audio data grows, leading to increased data storage and streaming requirements, necessitating more efficient audio data processing methods.
Innovation Solution
The method involves determining an importance metric for each audio object based on energy metrics, calculating a global importance metric, and iteratively adjusting quantization bit depths to maintain a signal-to-noise ratio threshold, allowing for adaptive quantization of audio signals and efficient encoding of audio data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of audio channels increases and speaker layout transitions from 2D to 3D, then the audio reproduction quality and spatial immersion are improved, but the data storage and streaming requirements increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the quantization bit depth for different audio objects based on their importance metrics. High-importance audio objects (such as dialogue) are allocated more bits (higher bit depth), while low-importance objects (such as background effects) receive fewer bits. This variable parameter approach optimizes the balance between audio quality and data efficiency in 3D spatial audio reproduction.
2Ease of manufacture
If uniform quantization bit depth is applied to all audio objects, then the implementation is simple, but the signal-to-noise ratio deteriorates for important audio objects
Solution Approach 1:
The patent implements local quality by assigning different quantization bit depths to different audio objects based on their individual importance metrics. Instead of applying a uniform quantization scheme across all audio objects, the system evaluates each object's significance (e.g., dialogue versus background effects) and allocates appropriate bit depth locally. This ensures high signal-to-noise ratio for important objects while maintaining overall system efficiency.
3Measurement precision
If adaptive quantization with importance metrics is implemented, then the signal-to-noise ratio for important audio objects is improved, but the processing complexity increases
Solution Approach 1:
The patent applies preliminary action by calculating importance metrics for all audio objects before the actual quantization process. The system pre-evaluates each audio object's significance (such as identifying dialogue versus background effects) and determines the appropriate quantization bit depth in advance. This preliminary classification simplifies the subsequent quantization process while ensuring optimal signal-to-noise ratio for important objects.
Data Source
AI summary
An importance metric, based at least in part on an energy metric, may be determined for each of a plurality of received audio objects. Some methods may involve: determining a global importance metric for all of the audio objects, based, at least in part, on a total energy value calculated by summing the energy metric of each of the audio objects; determining an estimated quantization bit depth and a quantization error for each of the audio objects; calculating a total noise metric for all of the audio objects, the total noise metric being based, at least in part, on a total quantization error corresponding with the estimated quantization bit depth; calculating a total signal-to-noise ratio corresponding with the total noise metric and the total energy value; and determining a final quantization bit depth for each of the audio objects by applying a signal-to-noise ratio threshold to the total signal-to-noise ratio.