Spectral Coefficient Entropy Coding for Harmonic Audio Contexts
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Solution Overview
Problem
Existing audio signal coding technologies face limitations in coding efficiency due to constraints on memory requirements, computational complexity, and robustness to channel errors, particularly for tonal signals where the context has to be too limited to exploit the harmonic structure effectively.
Innovation Solution
The proposed solution involves a context-adaptive entropy encoding method for spectral coefficients of an audio signal, where the relative spectral distance between previously decoded and currently decoded coefficients is adjusted based on information about the shape of the spectrum, such as pitch, inter-harmonic distance, or formant locations, to enhance entropy coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If context-based entropy coding uses a limited context to satisfy memory and computational constraints, then device complexity is reduced, but coding efficiency deteriorates because the context cannot exploit the harmonic structure of tonal signals
Solution Approach 1:
The patent applies local quality by making the context size adaptive rather than fixed. The context window dynamically adjusts its size based on the local spectral characteristics of the signal. For tonal signals with strong harmonic structure, the context expands to include more spectral neighbors, allowing exploitation of the harmonic pattern. For non-tonal signals, the context remains compact to save memory and computation. This resolves the contradiction by allowing large context only where needed locally, rather than maintaining large context everywhere.
Solution Approach 2:
The patent implements dynamics by making the context window size variable and adaptive. Instead of a static context size, the system dynamically adjusts the context window based on measured spectral properties such as spectral flatness or harmonic detection. This allows the coding system to transition between memory-efficient mode and high-efficiency mode depending on the signal content, resolving the contradiction between complexity constraints and coding efficiency requirements.
2Productivity
If the context window is expanded to exploit harmonic structure of tonal signals, then coding efficiency is improved, but memory requirements and computational complexity increase
Solution Approach 1:
The patent applies local quality by making the context size adaptive rather than fixed. The context window dynamically adjusts its size based on the local spectral characteristics of the signal. For tonal signals with strong harmonic structure, the context expands to include more spectral neighbors, allowing exploitation of the harmonic pattern. For non-tonal signals, the context remains compact to save memory and computation. This resolves the contradiction by allowing large context only where needed locally, rather than maintaining large context everywhere.
Solution Approach 2:
The patent implements dynamics by making the context window size variable and adaptive. Instead of a static context size, the system dynamically adjusts the context window based on measured spectral properties such as spectral flatness or harmonic detection. This allows the coding system to transition between memory-efficient mode and high-efficiency mode depending on the signal content, resolving the contradiction between complexity constraints and coding efficiency requirements.
3Speed
If low-overlap windows are used to decrease algorithmic delay, then processing speed is improved, but quantization noise increases due to important leakage in the MDCT
Solution Approach 1:
The patent applies feedback by using the decoded spectral coefficients to inform the entropy coding process. The entropy coder uses the previously decoded coefficients as context to predict the current coefficient, creating a feedback loop where decoding information feeds back into the coding process. This feedback mechanism allows the system to compensate for the increased quantization noise by exploiting statistical dependencies, thereby improving overall coding efficiency despite the noise introduced by low-overlap windows.
Data Source
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AI summary
A coding efficiency of coding spectral coefficients of a spectrum of an audio signal is increased by en/decoding a currently to be en/decoded spectral coefficient by entropy en/decoding and, in doing so, performing the entropy en/decoding depending, in a context-adaptive manner, on a previously en/decoded spectral coefficient, while adjusting a relative spectral distance between the previously en/decoded spectral coefficient and the currently en/decoded spectral coefficient depending on an information concerning a shape of the spectrum. The information concerning the shape of the spectrum may comprise a measure of a pitch or periodicity of the audio signal, a measure of an inter- harmonic distance of the audio signal's spectrum and/or relative locations of formants and/or valleys of a spectral envelope of the spectrum, and on the basis of this knowledge, the spectral neighborhood which is exploited in order to form the context of the currently to be en/decoded spectral coefficients may be adapted to the thus determined shape of the spectrum, thereby enhancing the entropy coding efficiency.