The PBX Scheduler Bug That Looked Like an ESP32 Audio Bug

I stopped treating this as an audio-quality problem when the device received media in repeating bursts that looked like a local queue or I2S starvation problem.

The test platform was the LOUP ESP32-S3 voice device with ES8311 playback, ES7210 capture, SIP/RTP media and a small speakerphone enclosure. The network codec was G.711 A-law/PCMA at nominal 8 kHz with 20 ms / 160-byte RTP payloads, while the physical audio path ran at 16 kHz in the recovered playback design. That mismatch between network time, device time and acoustic time is exactly why a vague word like “crackle” is not a diagnosis.

The evidence that matters for this article is specific: The one-vCPU PBX host showed about 75% steal, 80–160 ms scheduler wakeups and Asterisk timing failures; clean-host forwarding later fell to sub-millisecond ranges in short captures. I keep those observations tied to the build and test where they were recorded. They are not universal ESP32 performance claims.

The engineering result was also specific: The investigation moved upstream and confirmed a server scheduling failure before changing the device again. This article is about how I got from the symptom to that bounded conclusion, what the data did not prove, and what I would monitor before touching the same path again.

How I framed this case

I treated this case as a boundary-identification problem. The failure was audible at the end of the chain, but the useful question was which boundary first contained evidence of the defect. Starting from The device received media in repeating bursts that looked like a local queue or I2S starvation problem., I walked backward until The one-vCPU PBX host showed about 75% steal, 80–160 ms scheduler wakeups and Asterisk timing failures; clean-host forwarding later fell to sub-millisecond ranges in short captures. could either confirm or reject the current hypothesis. That approach kept later layers from being blamed for defects they only reproduced. The mechanism that mattered was A real-time media pipeline inherits timing from every scheduler it crosses, including the cloud VM running the PBX. The false lead—Assuming every burst visible at the ESP32 must have been created inside the ESP32.—was attractive precisely because it could explain the symptom without explaining the evidence. The accepted result, The investigation moved upstream and confirmed a server scheduling failure before changing the device again., mattered because it changed the earliest failing boundary rather than merely changing how the failure sounded.

Three timelines, not one latency number

I separate RTP media time, capture arrival time and execution time. RTP timestamps show where a packet belongs in the media timeline. PCAP arrival times show when the observation point received it. Firmware monotonic time shows when the device processed or played it. None is automatically synchronized with the microphone or loudspeaker in the room.

The PBX adds its own scheduler. If Asterisk receives a packet and forwards it 80 ms later, the endpoint cannot undo that delay. If the PBX forwards immediately but the ESP32 writes 80 ms later, the fault domain moves. Matched timestamps on both sides of the boundary are therefore more valuable than a single end-to-end “feels delayed” number.

Case notebook

I use this table as a compact incident contract. If a later retest changes the evidence but not the written conclusion, the conclusion needs review.

A useful follow-up is to ask what would falsify the retained result. For this case, a repeat run on the same controlled topology should reproduce the relevant observation. If The one-vCPU PBX host showed about 75% steal, 80–160 ms scheduler wakeups and Asterisk timing failures; clean-host forwarding later fell to sub-millisecond ranges in short captures. disappears while the symptom remains, then the old explanation no longer covers the new incident. If the observation returns without the symptom, then it may be contextual rather than causal. That is why I keep mechanism-level counters beside the listening test.

Instrumentation sketch

for each RTP packet:
  seq_delta       = seq[n] - seq[n-1]
  rtp_time_delta  = ts[n]  - ts[n-1]
  arrival_delta   = cap[n] - cap[n-1]
  pbx_forward_ms  = tx_time[n] - rx_time[n]

sequence answers ordering/loss
RTP timestamp answers media progression
capture time answers arrival/scheduling
none alone equals acoustic mouth-to-ear latency

The snippet is not presented as drop-in production code. It documents the measurement model. I want the instrumentation to be cheaper than the deadline it observes, explicit about units, and easy to disable or summarize after the call. The most dangerous diagnostic is one that silently changes scheduler behavior while appearing to measure it.

For The PBX Scheduler Bug That Looked Like an ESP32 Audio Bug, the next retest would therefore preserve the same topology and change only the variable tied to A real-time media pipeline inherits timing from every scheduler it crosses, including the cloud VM running the PBX.. I would collect the same observation again, compare it with the known-good control, and only then decide whether a new firmware branch deserves to replace the baseline.

Tools were chosen by the question, not by habit

The useful toolset for this layer was tcpdump/Wireshark/tshark-compatible RTP parsing, PBX scheduler/timer measurements, vmstat and matched receive/forward timestamps. I did not expect one tool to explain the whole call.

When the question was packet loss, I looked at RTP sequence and timestamps. When the question was device scheduling, I looked at monotonic callback and I2S timing. When the question was build identity, I used hashes and preserved artifacts. When the question was echo or timbre, packet capture stopped at the digital boundary and the next test had to include the physical speaker/microphone path.

That separation matters because every tool has a blind spot. UART logs can perturb timing. PCAP cannot hear the room. Far-end listening cannot prove which queue grew. Docker/Asterisk logs cannot prove the ESP32 played a sample. A codec detection scan cannot prove the channel mapping matches the DSP assumptions. The tool is evidence only for the layer it can actually observe.

This is also why I prefer small, named counters over giant debug dumps in realtime code. A counter such as “writes over 20 ms,” “max callback gap,” “queue underrun,” or “first callback to I2S start” has a defined semantic. It can be compared across builds without parsing thousands of lines whose own output may change the result.

The tempting explanation I did not accept

The attractive wrong turn was: Assuming every burst visible at the ESP32 must have been created inside the ESP32.

Embedded audio is full of these traps because many failure modes sound alike. Packet bursts, resampler artifacts, AEC suppression, output starvation, clipping and acoustic echo can all be described as “robotic” by a listener. A large queue can hide packet jitter while making conversation sluggish. Turning AEC off can remove one processing cost while making the product unusable as a speakerphone. A warning in the log can look causal simply because it is the only visible abnormality.

The rule I now use is that a theory must predict another observable fact. If I believe packet loss is causing a missing word, I should find the corresponding sequence gap or payload absence before the device. If I believe I2S is stalling, speaker-write timing should show it. If I believe the PBX is batching media, its receive/forward timestamps should expose the batch before the ESP32 sees it. If I believe echo is acoustic, changing volume or geometry should change the failure even when packet timing remains stable.

This is slower than guessing for the first ten minutes and much faster than carrying a wrong theory through ten firmware versions.

What this result proves—and what it does not

The result I am willing to claim is narrow: The investigation moved upstream and confirmed a server scheduling failure before changing the device again. It is supported by the recorded observation: The one-vCPU PBX host showed about 75% steal, 80–160 ms scheduler wakeups and Asterisk timing failures; clean-host forwarding later fell to sub-millisecond ranges in short captures.

It does not prove that every LOUP board, every network path or every future firmware build behaves the same way. It does not turn a server-side packet capture into an acoustic measurement. It does not turn a stable AEC-off test into permission to remove AEC from a speakerphone. It does not make a hash a quality metric. Those distinctions sound obvious in hindsight and are easy to lose when a demo deadline rewards a simple story.

The useful causal statement is the one consistent with the mechanism: A real-time media pipeline inherits timing from every scheduler it crosses, including the cloud VM running the PBX. If another experiment changes that mechanism, I expect the evidence to change too. If the evidence stays the same, I should question the theory before rewriting more code.

I also keep the rejected explanation visible: Assuming every burst visible at the ESP32 must have been created inside the ESP32. That is part of the result. Knowing which layer did not create the step change prevents future debugging from starting at the same dead end.

Reproducing the experiment without changing the experiment

If I had to hand this case to another engineer, I would ask them to preserve the same evidence boundary before attempting a fix:

• capture both receive and forward sides of the PBX where possible
• parse sequence, RTP timestamp and arrival time separately
• measure host scheduler/timer health before changing endpoint queues
• freeze jitter-buffer and endpoint settings for comparisons
• use a same-region PBX before attributing WAN delay to firmware

The point is not ceremony. Embedded audio is sensitive to hidden changes. A different softphone setting, a different PBX region, a new enclosure revision, a verbose log level or a queue added “for safety” can all change the result while leaving the test name unchanged.

For The PBX Scheduler Bug That Looked Like an ESP32 Audio Bug, the pass condition should be written in terms of the mechanism and evidence, not just “sounds good.” The subjective call still matters—it is the product—but the engineering result has to survive comparison.

The production rule that survived the incident

When timing is wrong, inspect every clock and scheduler in the path before redesigning the endpoint.

I translate that sentence into an operational rule. The metric or artifact that revealed the failure must remain available in future debug builds, but it must not create the same realtime cost. The known-good binary must remain recoverable. A new audio experiment must identify exactly which layer changed. A release candidate must be tested against the same user-visible behaviors that made the earlier baseline valuable.

This keeps debugging cumulative. Instead of starting every audio complaint with “maybe packet loss” or “maybe AEC,” the next investigation begins with a fault tree and a set of already-proven boundaries. The value of the V115/V127/V132A history is not the version numbers themselves; it is the accumulated map of which measurements are trustworthy and which changes have already regressed the product.

What I would do differently on the next product

I would design the measurement points earlier. The RTP callback, playout clock, queue, I2S boundary, exact AEC reference, capture slots and acoustic test points would all have named interfaces and low-cost counters from the beginning. That would reduce the amount of forensic reconstruction needed after subjective complaints arrive.

I would also separate debug verbosity from realtime instrumentation by architecture, not convention. Realtime code would update fixed counters or ring-buffer events; a lower-priority task would export summaries. If a UART or network logger can block the media task, the design has already allowed the observer into the deadline path.

For multi-device validation I would automate the call matrix and preserve a compact evidence bundle per run: firmware identity, hardware revision, test endpoint, RTP summary, device timing summary and subjective/acoustic result. The point is not to collect everything. It is to make two runs comparable.

Most importantly, I would preserve the same failure-model discipline. A real-time media pipeline inherits timing from every scheduler it crosses, including the cloud VM running the PBX. That principle remains true whether the next product uses ESP32-S3, a Linux SoC, a different codec or a cloud media service.

The result I keep from this incident

The investigation moved upstream and confirmed a server scheduling failure before changing the device again.

The deeper value is the method. I started with a perceptual symptom, located the earliest layer that could create it, chose evidence that could see that layer, changed one variable, and checked the known-good invariants afterward. The process is slower than random tuning for the first build and dramatically faster by the tenth.

The short version of the lesson is: When timing is wrong, inspect every clock and scheduler in the path before redesigning the endpoint.

That is the standard I now use for embedded audio work. A fix is not convincing because the call sounds better once. It is convincing when the mechanism, measurement, artifact identity and regression behavior all agree about why it got better.