With the rapid development of Internet technology, the amount of data flowing across the web has increased significantly. This has led to the emergence of video conferencing systems, which play a vital role in modern communication and collaboration. These systems rely heavily on voice transmission, making it one of the most critical performance indicators. Research into audio mixing algorithms is therefore essential for improving the quality and reliability of such systems.
One of the main challenges faced by terminals during the processing of chirp signals is how to mix and play multiplexed audio data locally. Synchronization issues, delays, and alignment with video can all impact the user experience. In practical applications, buffer overflow after mixing is a major concern, as it can lead to distortion and poor audio quality.
To address these issues, we introduce an improved mixing algorithm that enhances the performance of audio mixing in video conferencing systems. Compared to existing methods, this algorithm provides superior sound quality, reduces delay, and improves scalability. Experimental results show that it effectively suppresses overflow, maintains high-quality mixing, and is well-suited for real-time applications.
**1. Analysis of Mixing Algorithm**
Sound is a pressure wave generated by the vibration of objects, characterized by loudness, pitch, and timbre. In natural environments, sounds heard by the human ear are typically a combination of multiple sources. For video conferencing systems, audio signals from different participants must be mixed in the time domain. The sampling and quantization of voice signals are usually handled by the sound card chip, which commonly uses 16-bit resolution. However, when multiple channels are mixed, the resulting amplitude may exceed the sound card’s capacity, leading to distortion.
Several common approaches have been used to handle this issue:
- **Direct Clamping Method**: After mixing, any signal exceeding the buffer range is capped at the maximum value. While simple, this can cause unnatural peaks and noise.
- **Normalization Mixing**: The average of all channels is calculated to reduce overflow. However, this can result in lower volume and reduced clarity, especially when many users are speaking simultaneously.
- **Alignment Mixing**: This method adjusts the weight of each channel based on its intensity. Strong alignment emphasizes louder signals, while weak alignment focuses on quieter ones. Both approaches have their drawbacks, such as overpowering or amplifying background noise.
Despite these solutions, there is still room for improvement in terms of quality and efficiency. To address this, we propose a new and enhanced mixing algorithm.
**2. Improved Mixing Algorithm**
In SIP-based video conferencing systems, media streams can be mixed either centrally or at the terminal. Here, we implement a distributed mixing model, where the server only manages the conference system, while the terminal handles the actual mixing process. This approach reduces the load on the server and minimizes delay, making it ideal for real-time applications.
The algorithm is designed for small and medium-sized conferences, typically involving fewer than five participants. It takes into account the short-term correlation of speech signals (usually between 10ms and 30ms) and processes audio in frames. The algorithm flow includes initializing an attenuation factor, analyzing the signal, and dynamically adjusting the gain based on zero-crossing rates and energy levels.
This method ensures smooth mixing, reduces distortion, and avoids overflow by using normalized subdivision and dynamic adjustment of the decay factor. It also incorporates A-law encoding to enhance the accuracy of small signals, ensuring better sound quality and comfort for users.
**3. Embedded Implementation and Result Analysis**
To evaluate the performance of the improved algorithm, we tested it on an embedded platform using the TI DaVinci DM6446-594 processor. The algorithm was implemented on the ARM core, running at 297MHz. Three test signals were used: background noise, a moderately loud voice, and a voice close to the overflow threshold.
The results showed that the improved algorithm outperformed traditional methods. It produced smoother audio, minimized noise, and avoided overflow. Unlike other methods that require frequent recalculations and consume more resources, this algorithm efficiently handles frame-by-frame processing, reducing computational overhead.
**4. Conclusion**
By leveraging the characteristics of voice signals and implementing a frame-based attenuation strategy, the proposed algorithm effectively addresses the issue of audio overflow in video conferencing systems. It uses short-term energy and zero-crossing rate for detection and compensation, significantly improving the overall quality of the mix.
Users can choose between different algorithms depending on their network environment. The implementation on the ARM9 processor demonstrates that the algorithm is both efficient and effective, making it a promising solution for future video conferencing systems.
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