Multi-Reference LPC Filter Quantization for Low-Bitrate Speech Coding
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Solution Overview
Problem
Current speech and audio coding techniques, such as CELP, face challenges in efficiently quantizing LPC filters at low bit rates while maintaining subjective quality, especially in applications like multimedia streaming and wireless communication, where LPC filters are quantized separately in each frame with a fixed number of bits.
Innovation Solution
A multi-reference quantization method is introduced, where LPC filters are differentially quantized using various references, including past, future, or interpolated/future quantized filters, and absolute quantizers, to select the reference that minimizes bit rate or distortion, allowing for switched absolute or differential quantization modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If LPC filter quantization is performed separately in each frame with a fixed number of bits, then device complexity is reduced, but subjective quality deteriorates at low bit rates
Solution Approach 1:
The quantization process is segmented into multiple differential quantizers, each using a different reference filter. Instead of a single quantizer, the system divides the quantization task across multiple parallel quantizers (first differential quantizer, second differential quantizer, etc.), each specializing in different reference comparisons. This segmentation allows for more precise quantization while maintaining manageable device complexity through modular architecture.
Solution Approach 2:
The system changes the parameter of reference selection by using multiple different references instead of a single fixed reference. The reference selection criterion dynamically selects which reference to use based on current signal characteristics, allowing the quantization process to adapt to different speech conditions and maintain high quality across varying bit rate scenarios.
2Device complexity
If a fixed number of bits is used for LPC filter quantization, then device complexity is reduced, but bit rate efficiency deteriorates
Solution Approach 1:
The quantization process becomes dynamic through the reference selection mechanism. Instead of using a fixed number of bits with a static reference, the system dynamically selects among multiple references based on current signal conditions. This dynamic adaptation allows the quantizer to maintain efficiency across different speech scenarios without requiring complex device architecture.
Solution Approach 2:
The reference selection criterion acts as a feedback mechanism that evaluates current signal characteristics and selects the most appropriate reference. This feedback loop enables the system to adapt to changing speech conditions, improving bit rate efficiency by selecting references that minimize quantization error for the current frame's characteristics.
3Device complexity
If LPC filter quantization is performed without multi-reference differential quantization, then device complexity is reduced, but prediction residual error increases
Solution Approach 1:
The quantization task is segmented into multiple differential quantizers, each comparing the input LPC filter against a different reference. This segmentation allows the system to capture different aspects of the filter's variation, reducing the overall prediction residual error by distributing the quantization burden across multiple specialized comparators rather than relying on a single quantizer.
Solution Approach 2:
The system uses a composite quantization approach by combining multiple differential quantization results. Instead of relying on a single quantization path, the system synthesizes information from multiple quantizers using different references, creating a composite quantization result that has lower prediction residual error than any individual quantizer could achieve alone.
Data Source
AI summary
A multi-reference quantization device and method for quantizing an input LPC filter, comprises a plurality of differential quantizers using respective, different references, and a selector of a reference amongst the different references of the differential quantizers using a reference selection criterion. The input LPC filter is differentially quantized by the differential quantizer using the selected reference. A device and method for inverse quantizing a multi-reference differentially quantized LPC filter extracted from a bitstream, comprises an extractor from the bitstream of information about a reference amongst a plurality of possible references used for quantizing the multi-reference differentially quantized LPC filter, and a differential inverse quantizer using the reference corresponding to the extracted reference information to inverse quantize the multi-reference differentially quantized LPC filter.