Perceptual Weighter and Quantizer for IoT Audio Coding
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Solution Overview
Problem
Current speech and audio coders face inefficiencies in the Internet of Things (IoT) environment due to the need for explicit transmission of perceptual models, leading to increased bit-consumption and over-coding, especially in distributed sensor-networks where the same quantization is applied across multiple nodes.
Innovation Solution
The solution involves an encoding apparatus that recovers the perceptual model at the decoder without side-information by using a perceptual weighter and quantizer, which applies a random matrix to the input signal and calculates a sign function, and a decoding apparatus that de-quantizes and approximates the perceptual model using a pseudo-inverse and iterative methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the perceptual model is explicitly transmitted from each sensor node, then the decoder can access the model for accurate reconstruction, but the bit-consumption increases and over-coding occurs in distributed networks
Solution Approach 1:
The patent extracts only the essential characteristics of the perceptual model (spectral envelope parameters) rather than transmitting the complete model. The decoder reconstructs the full perceptual model from these extracted parameters, significantly reducing bit-consumption while maintaining reconstruction accuracy.
Solution Approach 2:
Instead of transmitting the actual perceptual model from each sensor node, the patent transmits a simplified representation (copy) of the model parameters. The decoder creates a local copy of the perceptual model based on these parameters, avoiding redundant transmission of identical model data across the network.
2Stability of the object's composition
If the same perceptual model is applied at all sensor nodes, then consistent coding is achieved, but over-coding occurs as the same information is transmitted multiple times
Solution Approach 1:
The patent merges the perceptual model information from multiple sensor nodes at the decoder side. Instead of each node transmitting identical model data, the decoder combines the transmitted parameters to reconstruct a unified perceptual model, eliminating redundant information while maintaining coding consistency.
Solution Approach 2:
The patent creates a universal perceptual model at the decoder that serves all sensor nodes simultaneously. This single reconstructed model can be used for decoding signals from multiple nodes, making the system more efficient while maintaining the consistency needed for accurate reconstruction.
3Measurement precision
If analysis-by-synthesis iteration is used to find optimal quantization, then perceptual distortion is minimized, but computational complexity increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-calculating and transmitting spectral envelope parameters that capture the essential characteristics of the perceptual model before the actual quantization process. This preliminary preparation allows the decoder to reconstruct the model without requiring complex iterative analysis-by-synthesis at decoding time.
Solution Approach 2:
The patent replaces the computationally expensive analysis-by-synthesis iteration with a simpler, lighter-weight reconstruction approach using transmitted parameters. This disposable approximation method achieves sufficient perceptual accuracy without the high computational cost of iterative optimization.
Data Source
AI summary
Disclosed is an apparatus for processing an input signal, having a perceptual weighter and a quantizer. The perceptual weighter has a model provider and a model applicator. The model provider provides a perceptual weighted model based on the input signal. The model applicator provides a perceptually weighted spectrum by applying the perceptual weighted model to a spectrum based on the input signal. The quantizer is configured to quantize the perceptually weighted spectrum and for providing a bitstream. The quantizer has a random matrix applicator and a sign function calculator. The random matrix applicator is configured for applying a random matrix to the perceptually weighted spectrum in order to provide a transformed spectrum. The sign function calculator is configured for calculating a sign function of components of the transformed spectrum in order to provide the bitstream. The invention further refers to an apparatus for processing an encoded signal and to corresponding methods.