Scalable Audio Codec for Multi-Point Conference Bit Rate Adaptation
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Solution Overview
Problem
Current audio codecs in conferencing applications face challenges with scalability, computational resource usage, and signal quality due to varying network conditions and endpoint capabilities, leading to increased latency and bandwidth requirements, especially in wireless networks and scenarios with packet loss.
Innovation Solution
A scalable audio codec that dynamically allocates bits between frequency bands based on energy ratios and prioritizes transform coefficients by importance, allowing for flexible bit rate adjustment and efficient packetization to maintain audio quality across different network conditions and endpoint capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional audio codecs are used with fixed bit rates, then audio quality is maintained, but scalability to different network conditions and endpoint capabilities is poor
Solution Approach 1:
The audio signal is divided into multiple frequency bands (e.g., low frequency band 0-14kHz and high frequency band 14kHz-22kHz), with each band encoded separately using transform coding. This segmentation enables independent bit rate allocation to different frequency components, allowing the codec to adapt to varying network conditions while maintaining manageable complexity through structured processing.
Solution Approach 2:
The codec employs dynamic bit allocation between frequency bands based on energy ratios calculated from the audio signal. The bit allocation is not fixed but adapts in real-time according to the signal characteristics and available bandwidth, enabling the system to optimize audio quality under different network conditions without requiring complex manual configuration.
2Manufacturing precision
If higher bit rates are used to maintain audio quality, then speech tonality and audio perception improve, but bandwidth requirements and computational resources increase
Solution Approach 1:
Different frequency bands are allocated different bit rates based on their importance and energy content. The low frequency band typically receives more bits than the high frequency band, as human perception is more sensitive to low frequency variations. This local quality differentiation allows maintaining good audio quality at lower overall bit rates by concentrating resources where they matter most.
Solution Approach 2:
The codec dynamically adjusts the bit allocation parameter between frequency bands based on the energy ratio of the audio signal. When the low frequency band has higher energy, more bits are allocated to it; when high frequency content is prominent, bits are reallocated accordingly. This parameter adaptation enables efficient use of bandwidth while maintaining perceived audio quality.
3Reliability
If redundant encoding is used to handle packet loss, then reliability improves, but computational resources and processing time increase
Solution Approach 1:
The codec performs preliminary bit allocation and transform coding in a structured manner that prepares the audio data for efficient packetization. By organizing the encoded data with clear frequency band separation and importance ordering, the system can handle packet loss through selective decoding without requiring redundant encoding of the entire signal, thus maintaining reliability while improving processing efficiency.
Data Source
AI summary
Use of a scalable audio codec to implement distributed mixing and/or sender bit rate regulation in a multipoint conference is disclosed. The scalable audio codec allows the audio signal from each endpoint to be split into one or more frequency bands and for the transform coefficients within such bands to be prioritized such that usable audio may be decoded from a subset of the entire signal. The subset may be created by omitting certain frequency bands and/or by omitting certain coefficients within the frequency bands. By providing various rules for each endpoint in a conference, the endpoint can determine the importance of its signal to the conference and can select an appropriate bit rate, thereby conserving bandwidth and/or processing power throughout the conference.