Audio Encoding Using Vector Quantized Residual Error Features
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Solution Overview
Problem
Existing audio coding technologies at low bit rates suffer from coding artifacts like pre-echo and quantization noise, limiting the range of sound quality improvement, as current methods rely on limited additional information such as presence or absence of voice or transient signals.
Innovation Solution
An audio encoding apparatus and method that uses a neural network to encode residual error features, vector quantize them, and transmit these as additional information, enabling end-to-end deep learning for joint training of models to encode, decode, and post-process the features, thereby improving sound quality without the limitations of existing methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional audio coding is used at low bit rates, then transmission efficiency is improved, but coding artifacts such as pre-echo and quantization noise occur degrading audio quality
Solution Approach 1:
The patent segments the audio coding process into multiple independent components: main codec encoding, residual error calculation, neural network-based residual encoding, and post-processing. By dividing the audio signal processing into these separate stages, each component can be optimized independently, allowing low bit rate transmission while maintaining audio quality through targeted error correction at each segment.
Solution Approach 2:
The patent introduces several intermediary elements: residual error signals that capture the difference between original and decoded audio, neural network models that learn optimal error patterns, and post-processing modules that refine the decoded output. These intermediaries act as mediators between the compressed audio and the final output, enabling quality improvement without increasing the base bit rate.
2Object-affected harmful factors
If additional information is used to improve sound quality, then audio quality is improved, but the range of sound quality improvement is limited due to restricted types of additional information
Solution Approach 1:
The patent transitions from traditional scalar additional information (presence/absence flags, gain values) to vector-based residual error features. By representing additional information as multi-dimensional vectors captured by neural networks, the system gains access to a much richer feature space that can capture complex audio characteristics beyond simple presence detection or gain adjustment.
Solution Approach 2:
The patent changes the fundamental parameters of additional information from discrete, limited values to continuous, high-dimensional residual error vectors. This parameter transformation allows the additional information to encode subtle audio characteristics, transient features, and spectral details that were previously impossible to represent with traditional additional information formats.
3Object-affected harmful factors
If multiple processing stages are used to improve audio quality, then audio quality is improved, but errors accumulate sequentially across encoding and decoding stages
Solution Approach 1:
The patent implements feedback mechanisms where the decoded audio is re-encoded to generate residual errors, which are then used to guide further processing. This feedback loop allows the system to identify and correct errors at each stage rather than allowing them to accumulate. The neural network models are trained to minimize the residual error between consecutive processing stages, ensuring error correction rather than accumulation.
Solution Approach 2:
The patent performs preliminary encoding and error calculation before final decoding and post-processing. By pre-calculating residual errors and training neural network models on these errors in advance, the system prepares correction mechanisms that can be applied systematically during decoding, preventing error accumulation rather than addressing it after the fact.
Data Source
AI summary
An audio encoding/decoding apparatus and method using vector quantized residual error features are disclosed. An audio signal encoding method includes outputting a bitstream of a main codec by encoding an original signal, decoding the bitstream of the main codec, determining a residual error feature vector from a feature vector of a decoded signal and a feature vector of the original signal, and outputting a bitstream of additional information by encoding the residual error feature vector.