将I2S音频信号转为声音,用听觉检测传输故障。
Sonifying I2S Transport Signals to Detect Transmission Faults
- 通过分频处理时钟、帧同步和数据流到左右声道,实现信号结构听觉化。
- 混合结构与数据信息的联合表示可提升故障分类分离度。
- 适合硬件调试人员或对听觉感知故障敏感的工程师使用。
本文提出一种基于声学化的设计,用于支持I2S传输信号的故障检测。I2S是集成电路间实时数字音频通信的常用协议,但缺乏内置错误检测机制。由于传输故障常表现为时序、帧对齐等问题,传统视觉方法难以识别。本设计采用过采样技术进行时间重缩放,将时钟信号(SCK)、帧信号(WS)和数据信号(SD)分别映射至立体声双通道,形成可听化表示。通过计算评估不同负载类型与故障条件(如抖动、位移错位、字长错误)下的特征空间可分离性,结果表明:虽然过采样会系统性改变特征值,但未显著提升故障类间的可分离性;然而,结构与数据信息在双通道联合表示下展现出稳定且适度的分离度提升。研究提示,声学化通信协议数据的可分离性更依赖于互补信息流的融合,而非单纯信号缩放。
原文摘要 · Abstract (English)
This paper outlines a sonification design to support fault detection in the transmission of I2S transport signals. I2S is a protocol for communicating real-time digital audio between integrated circuits that, while in wide and general use, does not include built-in error detection. Moreover, given the nature of the protocol transmission faults affecting timing, framing and alignment can be difficult to identify using conventional visual methods. The proposed design addresses this with an approach informed by Audification, wherein oversampling controls temporal rescaling to render protocol structure (SCK and WS) and payload data (SD) across separate stereo channels. A preliminary computational feasibility study was carried out to measure feature-space separability of I2S faults in the generated auditory representations as opposed to listener performance. It evaluates the design across several payload types and error conditions including jitter, bit-slip, and word-length errors. Class separability was assessed through clustering analyses of extracted features. The evaluation results show that while oversampling produces systematic changes in feature values, it does not meaningfully improve separability between error classes. However, a modest but consistent improvement in separability is observed as a function of the joint representation of structural and payload information across channels. The findings suggest that feature-space separability in sonified communication protocol data may be dependent on the integration of complementary information streams, rather than on signal scaling alone.
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