How to Streamline RTC Audio Issue Troubleshooting: Frameworks, Tools, and Automation

Background and Challenges

Real‑Time Communication (RTC) audio technology transmits audio streams instantly to remote users and is widely used in online education, video conferencing, live streaming, social entertainment, finance, healthcare, and government scenarios. In practice, the diversity of audio issues, their varied root causes, and the need for rapid diagnosis create significant challenges for developers and operators.

Common Audio Issue Manifestations

Typical audio problems include noise, echo, distortion, low volume, and synchronization issues, all of which can severely degrade user experience or even prevent communication.

Main Characteristics of Audio Issues

High randomness : Network conditions (2G/3G/4G/5G/Wi‑Fi) and device usage vary, causing unpredictable audio problems.

Platform and device diversity : Support is needed for Windows, Android, iOS, macOS, IoT, and various peripherals such as Bluetooth headsets and sound cards.

Identical symptoms, multiple root causes : The same noise may stem from packet loss, CPU overload, or algorithmic shortcomings, making diagnosis difficult.

Strict user tolerance : Users expect low latency, high fidelity, stability, and reliability in audio transmission.

Audio Issue Troubleshooting Process

The process involves identifying the source of the problem, the responsible parties, and the handling methods, as illustrated in the diagram below.

Pain Points in Audio Issue Diagnosis

Difficulty obtaining data : Real‑time issues are reported after the fact, making it hard to capture raw audio data or reproduce problems.

Complex troubleshooting workflow : Multiple stages such as data collection (ADM), processing (APM), codec handling (ACM), network monitoring (ANM), server analysis, and user context are involved.

High technical threshold : Requires knowledge of audio and signal processing, and the internal tools have a learning curve.

Fragmented and diverse problems : Issues like echo, noise, and distortion each need specific investigation methods.

Practice: Standardizing and Automating Audio Issue Handling

The team focused on standardizing and automating the audio issue handling workflow to improve efficiency.

Standardization Steps

Clarify audio data flow and control flow.

Enhance the usability of the RTC QS (Quality Service) investigation tool.

Disseminate audio knowledge, including data flow, control flow, and common troubleshooting cases.

Monitor issue progress and bottlenecks via JIRA integration.

Automation Practices

The QS tool and supporting infrastructure collect, classify, aggregate, and retrieve RTC audio data, enabling automatic perception of various problem types.

Device/silence anomaly detection : Maps SDK device and silence states to assess stability.

Low volume detection : Correlates volume levels with audio quality.

Unstable recording/playback frequency detection : Identifies jitter that leads to audio stutter.

Echo detection : Monitors long‑term echo cancellation failures.

Audio stutter detection : Highlights interruptions in playback.

High latency detection : Flags delays beyond 200 ms that affect user perception.

Audio‑video desynchronization detection : Uses defined criteria to spot sync issues.

Automatic Diagnosis

By combining perception models across the audio pipeline and considering client scenarios, the system provides preliminary diagnoses that reduce reliance on manual QS checks and accelerate root‑cause identification.

Outlook

Improving the efficiency of RTC audio troubleshooting remains a key industry goal. Future work will focus on iterating the detection systems for various audio problems, enhancing accuracy and sensitivity, integrating automatic alerts with JIRA, expanding internal usage to customer‑facing tools, and leveraging AIGC for deeper analysis and assistance.