This post summarises a manuscript submitted with Benjamin Bentz and colleagues from the RAPP Lab network. The paper is not yet peer-reviewed; numbers and conclusions are based on operational measurements collected 2020–2023. Feedback welcome — particularly from anyone who has run similar measurements on non-European or wireless-last-mile links.
Evidence checked through 2026-07-11. Exact LoLa and MVTP build identifiers were not preserved in the public article, so the measurements characterise the tested deployments rather than every release of either platform.
The Problem
Musicians playing together in the same room experience acoustic propagation delay of roughly 3 ms per metre of separation — essentially free latency that most ensembles never consciously register. When you distribute musicians across a network, you inherit that propagation cost plus everything the signal chain adds on top: buffers, codec processing, routing hops, switching overhead.
General-purpose conferencing systems optimise for robustness and conversation, not phase-locked ensemble timing; their delay also varies with software, device, route and network conditions. NMP studies report task-dependent latency tolerance rather than one universal cutoff. Tempo, instrument, repertoire, cueing and adaptation all matter [Carôt 2011; Tsioutas & Xylomenos 2021; Medina Victoria 2019].
Specialised low-latency systems cut the processing overhead by avoiding compression, using hardware-accelerated video pipelines, and riding research-and-education networks that offer better jitter characteristics than commodity internet. Two of the better-known ones are LoLa (Low Latency Audio Visual Streaming System, developed at Conservatorio G. Tartini Trieste) and MVTP (Modular Video Transmission Platform, developed at CESNET in Prague). We deployed both at Hochschule für Musik und Tanz Köln as part of the RAPP Lab collaboration and spent about two and a half years measuring them.
The Latency Budget
End-to-end latency in NMP is cumulative and non-recoverable. Once delay enters the chain, nothing downstream can subtract it. The budget looks like:
\[ L_\text{total} = L_\text{capture} + L_\text{buffer} + L_\text{network} + L_\text{playback} \]Network latency \( L_\text{network} \) includes propagation (roughly \( d / (2 \times 10^8) \) seconds for a fibre link of distance \( d \) metres, accounting for the refractive index of glass) plus per-hop processing. Everything else is system-dependent.
The key insight is that \( L_\text{buffer} \) is not fixed. Greater variation in packet arrival times may require more buffering to avoid underruns, which adds latency. Link capacity still matters: the uncompressed streams must fit with headroom. Once that condition is met, jitter, loss, route stability and endpoint buffering can matter more than the headline bit rate.
What We Measured and How
Network RTT. ICMP ping, 1,000 packets per run. We report the median as a robust summary; the mean is too sensitive to the occasional rogue packet.
End-to-end audio latency. An audio signal-loop: transmit a test signal from site A to site B, have site B return it immediately, estimate round-trip delay by cross-correlation. One-way latency = signal-loop RTT / 2. This method captures local processing and buffering at both ends in addition to the network leg, which is what actually matters for a musician. Dividing by two estimates one-way delay only if the outward and return paths and endpoint contributions are sufficiently symmetric; the values below should be read with that assumption, not as direct one-way measurements.
Video latency. Component-based estimation (capture frame cadence + processing pipeline + display). We did not have a frame-accurate video loopback method, so treat these numbers as estimates rather than precision measurements. That caveat matters less than it might seem because, as you will see, video was always slower than audio by a wide enough margin that it did not drive the operational decisions.
Firewall impact. A 4-hour comparison on the Cologne–Vienna link, alternating between the site’s approved direct research-network path and a transparent enterprise-firewall path, logging packet loss and decoder instability. This was one configuration comparison, not a firewall-class test.
Six partner institutions, air distances from 175 to 1,655 km, measurements collected between October 2020 and March 2023.
Results
Audio latency
| Partner (from Cologne) | Air distance (km) | Median RTT (ms) | One-way audio latency (ms) |
|---|---|---|---|
| Prague | 535 | 5.0 | 7.5 |
| Vienna | 745 | 7.0 | 9.5 |
| Detmold | 175 | 7.5 | 10.0 |
| Trieste | 775 | 10.0 | 12.5 |
| Rome | 1,090 | 17.5 | 20.0 |
| Tallinn | 1,465 | 19.5 | 22.0–22.5 |
The number that jumps out immediately: Detmold (175 km away) has higher latency than Vienna (745 km away). Route and endpoint differences can explain why air distance does not order this small sample. Our route observations were consistent with a less direct campus path at Detmold and a shorter path to Prague, but the measurements do not isolate each hop or endpoint contribution.
The practical implication is narrower: within these six deployments, air distance alone was a poor predictor of achieved latency. Measure the actual path and endpoint system; a map supplies only a propagation lower bound.
Video latency
Estimated one-way video latency was 20–35 ms across all configurations, with the dominant contributions coming from frame cadence (at 60 fps, you wait up to 16.7 ms for a frame to be captured regardless of what the network is doing) and buffering at the decoder. In these deployments, the video estimate was consistently above the audio estimate. Session observations suggested that musicians relied mainly on audio for synchronisation and used video as a supplementary expressive and social cue. That is an operational observation, not a controlled cue-use study.
The firewall experiment
This is the result I find most important for anyone planning a similar deployment.
| Metric | DMZ (no firewall) | With enterprise firewall | Change |
|---|---|---|---|
| Dropped audio packets | 0.002% | 0.052% | +26× |
| Audio buffer realignments/hour | 0.3 | 3.9 | +13× |
| Dropped video frames | 0.04% | 0.74% | +18× |
| Additional latency | — | 0.5–1.0 ms | — |
The raw latency increase (0.5–1.0 ms) was small relative to the packet-loss and buffer-event differences. This comparison establishes an association with the tested firewall path. It does not identify deep packet inspection, stateful tracking, congestion, or another mechanism, and it cannot be generalised to other products or policies from one session.
The message is to test the institutionally approved end-to-end path under representative load. In this session, the observed penalty appeared mainly as stability rather than raw delay; its size is specific to this configuration.
Discussion
Based on the measured latencies, literature and reports from our sessions, I would cautiously characterise these particular links as follows. These are not controlled perceptual thresholds or guarantees for a repertoire class:
Prague, Vienna, Detmold, Trieste (7.5–12.5 ms): Supported demanding chamber-music work in our sessions. Some participants described the interaction as “natural” or “like being in the same room”; the article does not provide a validated survey instrument or sample size.
Rome (20 ms): Slower movements and music where tight rhythmic locking was not the primary aesthetic concern were workable in our sessions. Participants reported more difficulty in rhythmically dense, fast passages.
Tallinn (22–22.5 ms): A March 2023 concert collaboration was completed, with musicians reporting adaptation toward anticipation rather than purely reactive synchronisation.
What is notably absent from this data: anything outside the European research-network context. All six links ran on GÉANT or national backbone equivalents with favourable jitter characteristics. The numbers almost certainly do not transfer directly to commodity internet, satellite links, or mixed-topology paths.
Limitations I want to be explicit about. The video latency estimates are component-based, not directly measured, so treat that 20–35 ms range with appropriate skepticism. The firewall comparison is a single 4-hour session on a single link; I would not want to extrapolate too aggressively to other firewall vendors or configurations. And this is an operational measurement study, not a controlled perceptual experiment — I cannot tell you from this data at precisely what latency threshold a given ensemble will declare a session unusable, because that depends on the music, the musicians, and factors I did not measure.
Practical Takeaways
For anyone setting up a similar system:
- Measure the full signal path before committing to a collaboration. Air distance and ICMP RTT do not capture endpoint processing or jitter by themselves.
- Plan an approved low-jitter path with network and security staff. The single firewall comparison motivates local testing, not a generic security bypass or DMZ prescription.
- Characterise the campus route. Hop count alone does not determine jitter; measure queues, loss and variation under representative load.
- Tune buffers empirically and monitor underruns. The smallest buffer is not always the best buffer; choose against the observed stability/latency trade-off.
- Measure audio/video alignment in the actual deployment. Video lagged audio in these configurations, but that is not an invariant of every pipeline.
References
Carôt, A. (2011). Low latency audio streaming for Internet-based musical interaction. Advances in Multimedia and Interactive Technologies. https://doi.org/10.4018/978-1-61692-831-5.ch015
Drioli, C., Allocchio, C., & Buso, N. (2013). Networked performances and natural interaction via LOLA. LNCS, 7990, 240–250. https://doi.org/10.1007/978-3-642-40050-6_21
Medina Victoria, A. (2019). A method for the measurement of the latency tolerance range of Western musicians. Ph.D. dissertation, Cork Institute of Technology (now Munster Technological University).
Rottondi, C., Chafe, C., Allocchio, C., & Sarti, A. (2016). An overview on networked music performance technologies. IEEE Access, 4, 8823–8843. https://doi.org/10.1109/ACCESS.2016.2628440
Tsioutas, K. & Xylomenos, G. (2021). On the impact of audio characteristics to the quality of musicians experience in network music performance. JAES, 69(12), 914–923. https://doi.org/10.17743/jaes.2021.0041
Ubik, S., Halak, J., Kolbe, M., Melnikov, J., & Frič, M. (2021). Lessons learned from distance collaboration in live culture. AISC, 1378, 608–615. https://doi.org/10.1007/978-3-030-74009-2_77
Changelog
- 2026-07-11: Bounded latency and repertoire claims to the measured deployments; documented the signal-loop symmetry assumption; removed an unsupported firewall-mechanism inference and the generic DMZ recommendation.
- 2026-01-20: Updated the Drioli et al. (2013) LNCS volume number to 7990 (ECLAP 2013 proceedings). Updated the Ubik et al. (2021) AISC volume number to 1378 and page range to 608–615. Updated the fifth author’s surname to “Frič.”