Sonifying I2S Transport Signals to Detect Transmission Faults

2026-08-17Sound

Sound
AI summary

The authors designed a way to turn digital audio communication signals (I2S) into sound, aiming to help detect faults that are hard to spot visually. They separate different parts of the signal into stereo channels and tested if this makes error types easier to recognize using computer analysis. Their study found that simply speeding up the sound (oversampling) didn't help identify errors better, but combining both the structure and data parts across channels gave a small improvement. This suggests that mixing different pieces of information in the sound is more useful than just changing the signal speed for spotting faults.

I2S protocolsonificationaudificationfault detectionoversamplingjitterbit-slipword-length errorfeature-space separabilityclustering analysis
Authors
Stephen Roddy
Abstract
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.