Audio Encoder Excitation Selection for Mixed Signals
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Solution Overview
Problem
Existing audio coding methods face challenges in selecting the optimal compression method for signals that contain both speech and music, as current algorithms often fail to distinguish between the two effectively, leading to suboptimal compression and quality issues.
Innovation Solution
An algorithm that analyzes long-term prediction parameters to select between ACELP and TCX excitation methods based on signal characteristics, such as periodicity and transients, to determine the best coding method for each frame of the audio signal, improving sound quality without significantly affecting compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single compression algorithm is used for all audio signals, then device complexity is reduced, but sound quality deteriorates for mixed speech-music signals
Solution Approach 1:
The system dynamically switches between ACELP and TCX excitation methods based on real-time analysis of LTP parameters. The encoder analyzes long-term prediction parameters to detect signal characteristics (periodicity, transients) and adaptively selects the appropriate excitation method for each frame, making the compression algorithm dynamic rather than static.
Solution Approach 2:
The system changes the excitation method parameter based on analyzed signal properties. By examining LTP parameters such as periodicity and transient characteristics, the system adjusts the excitation method (ACELP or TCX) to match the signal type, optimizing compression performance for different signal segments.
2Manufacturing precision
If different compression algorithms are used for speech and music, then sound quality is improved, but device complexity increases due to algorithm selection
Solution Approach 1:
The system uses LTP parameters (periodicity, transients) as decision parameters to select between ACELP and TCX excitation methods. This parameter-based approach provides a systematic way to differentiate between speech and music characteristics without requiring complex classification algorithms.
Solution Approach 2:
The patent replaces complex speech-music classification mechanisms with a simpler system based on LTP parameter analysis. Instead of using sophisticated recognition methods to classify signal types, the system substitutes this with direct analysis of long-term prediction parameters that naturally differentiate between periodic (speech) and aperiodic (music) signals.
3Measurement precision
If LTP analysis is performed to distinguish signal types, then excitation method selection accuracy is improved, but processing time increases
Solution Approach 1:
The LTP analysis is performed in advance during the encoding process to prepare excitation method selection. By analyzing long-term prediction parameters beforehand, the system determines the appropriate excitation method before actual compression, enabling efficient real-time processing without sacrificing accuracy.
Data Source
AI summary
The invention relates to an encoder (200) comprising an input (201) for inputting frames of an audio signal, a LTP analysis block (209) for performing a LTP analysis of the frames of the audio signal to form LTP parameters on the basis of the properties of the audio signal, and at least a first excitation block (206) for performing a first excitation for frames of the audio signal, and a second excitation block (207) for performing a second excitation for frames of the audio signal. The encoder (200) further comprises a parameter analysis block (202) for analysing said LTP parameters, and an excitation selection block (203) for selecting one excitation block among said first excitation block (206) and said second excitation block (207) for performing the excitation for the frames of the audio signal on the basis of the parameter analysis. The invention also relates to a device, a system, a method, a module and a computer program product.