With the rapid advancement of interconnected technologies, the amount of data processed across these networks has been steadily increasing. Video conferencing systems have emerged as a key solution for real-time communication, where audio transmission—especially voice clarity and synchronization with video—has become a critical performance metric. Within these systems, the mixing algorithm plays a vital role in managing multi-channel audio signals to ensure smooth playback and minimize distortion.
One of the primary challenges in terminal processing is how to mix and play back multi-channel audio locally while maintaining synchronization with the video stream. A common issue is buffer overflow on the sound card after mixing, which can lead to audio distortion or loss. To address this, various methods have been proposed, including direct clamping, normalization, and alignment techniques. However, each of these approaches has limitations, such as signal distortion, reduced volume, or increased background noise.
To improve upon existing solutions, an enhanced mixing algorithm has been developed, particularly suitable for small-scale video conferencing environments like SMEs and schools, where the number of participants is limited (typically under five). This algorithm takes into account both short-term correlation of speech signals and dynamic adjustments based on frame-based analysis.
The improved algorithm uses a distributed mixing model, where the server manages the conference setup rather than processing media streams directly. Each terminal receives and decodes the audio, then performs local mixing. This reduces server load and minimizes echo, leading to better overall performance in real-time applications.
The algorithm works by initializing an attenuation factor, analyzing the maximum peak value and zero-crossing rate within each frame, and dynamically adjusting the attenuation based on these parameters. It ensures that the mixed audio remains within the sound card's capacity without significant distortion. The use of frame-based processing also reduces computational overhead compared to sample-by-sample methods.
Testing the algorithm on a TI DaVinci DM6446-594 processor showed promising results. Compared to traditional methods, the improved algorithm produced smoother audio with less noise, no clipping, and no overflow issues. It demonstrated better performance in terms of quality and efficiency, especially in scenarios with low participant interaction.
In conclusion, the proposed algorithm addresses the problem of audio mixing overflow by leveraging speech signal characteristics and frame-based processing. It enhances mixing quality through dynamic attenuation and provides flexibility for different network conditions. The implementation on a fixed-point ARM9 processor confirmed its effectiveness, making it a viable solution for real-time video conferencing systems.
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