Pith. sign in

REVIEW 3 major objections 7 minor 12 references

Self-Mixing Laser Interferometry: In Search of an Ambient Noise-Resilient Alternative to Acoustic Sensing

T0 review · 3 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read In frequency-domain tests, a self-mixing laser fingertip clearly outperforms a microphone under targeted ambient noise while keeping a smaller edge under broadband noise.

desk verdict An honest, incremental validation study of SMI versus acoustic sensing for robot contact detection; the headline 'clear winner' claim is mostly supported but overgeneralized in the targeted-noise section. read the letter →

arxiv 2504.13711 v1 pith:ZEPIBIKX submitted 2025-04-18 cs.RO

Remko Proesmans , Thomas Lips , Francis wyffels This is my paper · ORCID
classification cs.RO
keywords self-mixinginterferometrytactilesensingacousticextrinsiccontactdetectionnoiseresilienceaudiospectrogramtransformerroboticmanipulationfrequency-domainclassification
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether a self-mixing laser interferometry (SMI) fingertip, which detects microvibrations through light reflected back into a laser cavity, can serve as an ambient-noise-resilient replacement for a microphone in robotic manipulation. It extends the authors' earlier time-domain comparison with a frequency-domain classification task: a robot shakes a cup and a classifier must tell whether the cup is empty, holds bolts, or holds playdough, while music, white noise, or the sound of another cup being shaken nearby are played. The paper finds that SMI still outperforms acoustic sensing under broadband ambient noise, though the margin is smaller than the time-domain analysis suggested, and that under targeted disturbances that mimic the desired signal the laser is the clear winner. It also reports that motor noise spreads across the SMI spectrum more than the microphone spectrum, and concludes that raising the SMI readout frequency is the main next step.

What carries the argument

The load-bearing mechanism is the self-mixing fringe: every time the target moves $\lambda/2$, the optical path difference changes by one wavelength and a discontinuity appears in the photodiode current, so a sinusoidal vibration becomes a signal with the same fundamental frequency plus extra higher harmonics. This frequency-spreading signature is what gives SMI its isolation from airborne ambient sound, and it is also why motor noise bleeds across the laser spectrum. The frequency-domain evaluation is carried by the Audio Spectrogram Transformer, a transformer classifier pretrained on audio spectrograms, applied identically to 128-bin log-mel spectrograms from both sensors.

What would settle it

Retrain the classifiers separately for each sensor with sensor-specific features and with targeted-noise examples added to the training set, then rerun the targeted-disturbance test sets; if the microphone accuracy rises to the laser's level, the claim that SMI is the clear winner under in-band noise would not hold.

Watch

Extended reading notes

Core claim

The paper's central claim is that for targeted noise disturbances, analogous to multiple robots collecting data for the same task in the same environment, self-mixing interferometry is still the clear winner over acoustic sensing. In the frequency-domain experiment the laser reaches $0.92 \pm 0.15$ accuracy on targeted-disturbance test sets while the microphone falls to $0.34 \pm 0.42$; on broadband ambient noise the laser scores $0.95 \pm 0.03$ versus $0.91 \pm 0.10$, so it outperforms but less decisively than the time-domain SNR numbers implied. The laser's dominant error under broadband noise is confusing bolts for playdough, while the microphone's dominant error is confusing an empty cup for playdough. The paper also establishes that motor noise from the robot wrist floods the entire SMI spectrum rather than staying in narrow bands, and that this frequency spreading is fundamental to the SMI fringe mechanism.

Load-bearing premise

The comparison assumes that one audio-pretrained classifier, applied to the same kind of sound-frequency image for both sensors, judges the laser fairly even though the laser spreads frequencies differently than a microphone does.

Editorial extensions

If this is right

  • In multi-robot or shared environments where nearby agents produce sounds similar to the contact events being sensed, an SMI fingertip can keep a cup-content classifier near 90 percent accuracy, while a microphone-based classifier can fall to near zero on the same trials.
  • Even under broadband noise, a microphone can still classify events at roughly 90 percent accuracy, so the SMI advantage there is modest rather than decisive.
  • Because motor noise spreads across the SMI spectrum, the paper's stated priority of raising the readout frequency above the current 18 kHz is necessary for separating contact signals from robot self-noise.
  • The frequency-spreading effect implies that features distinguishing contact events may currently sit in unmeasured bands, so a higher sampling rate could reveal separations the present readout cannot see.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not test a sensor-specific classifier; because the laser's spectrogram is not natural audio, a different feature representation could change the ranking, especially on broadband noise where the margin is only a few accuracy points.
  • The motor-noise result suggests that simply raising the readout frequency may not suffice; modeling the laser's nonlinear fringe transfer function could be needed to separate contact-induced harmonics from motor noise.
  • A direct extension is to add targeted-noise examples to the training set; since the paper trains only on clean data, the microphone's collapse on targeted disturbances may partly reflect a classifier blind spot rather than a pure sensing limitation.
  • Because the two sensors show different error modes, a combined fingertip with both SMI and a microphone could classify contact events more reliably than either alone under mixed noise.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The paper presents an experimental comparison of two robotic fingertip sensing modalities for extrinsic-contact sensing: self-mixing laser interferometry (SMI) and a bottom-port microphone, extending the authors' prior work [8] from the time domain to frequency-domain validation. Section III reports time-domain signal-to-noise measurements: silicone drops under white-noise playback (laser better by 13.5 dB) and bolt drops into a second cup held by a person (laser achieves an additional 6.4 dB of in-band isolation relative to the microphone, with claimed perfect separation). Section IV introduces a three-class classification task (empty cup / bolts / playdough) using an AudioSet-pretrained Audio Spectrogram Transformer fine-tuned per sensor, trained on 150 clean trials and tested on eight 10-trial disturbance sets: four ambient (music/white noise) and four targeted (a person shaking a nearby cup containing bolts or playdough, with the robot cup empty). Main reported results: validation accuracy 1.00 for both sensors; ambient disturbances 0.91±0.10 (microphone) vs 0.95±0.03 (laser); targeted disturbances 0.34±0.42 (microphone) vs 0.92±0.15 (laser), with strong per-scenario variation (microphone 0.00 on both bolts scenarios and 1.00 on playdough-mimicking; laser 0.86 on playdough-mimicking and 1.00 on playdough-shaking). Section V discusses motor noise, which floods the SMI spectrum, supporting a recommendation for higher SMI readout rates.

Significance. If the results hold, this is a useful and unusually well-documented sensor comparison. Strengths: the test sets are held out and contain disturbance conditions never seen during training; both sensors record simultaneously, enabling a paired comparison; the time-domain SNR measurements are independent of any learned model and corroborate the direction of the classification results; identical preprocessing, architecture, and pretraining for both sensors make the classifier comparison controlled; and the audio-native pretraining plausibly biases the evaluation against the SMI modality, making the laser's performance a conservative lower bound. The paper's honest discussion of the reduced broadband advantage and of the laser's motor-noise vulnerability is another strength.

major comments (3)
  1. [Table I and Section IV-C (also abstract and conclusion)] The targeted-disturbance claim aggregates four 10-trial scenarios into 0.92±0.15 vs 0.34±0.42, but the reported standard deviation is computed over the five cross-validation models, which all score the same ten test trials, so it is not a trial-level sampling uncertainty, and no significance test is reported. With n=10 per scenario, binomial uncertainty is large: in the 'Playdough, mimicking robot' row the laser's 0.86±0.22 is statistically indistinguishable from the microphone's 1.00±0.00, and this row—the closest embodiment of the abstract's 'multiple robots simultaneously collecting data for the same task' analogy—goes against the aggregate direction. The aggregate is driven almost entirely by the two bolt rows, where the microphone scores 0.00. The same lack of inferential statistics affects the broadband claim: 0.95±0.03 vs 0.91±0.10 have overlapping ranges, and the white-noise row favors the microphone (1.00 vs 0.97). The authors should report per-scenario and aggregate trial-level inference (e.g., McNemar tests on the 10 paired trials, since both sensors record simultaneously, or bootstrap confidence intervals over trials), state the scenario mix that defines 'targeted disturbances', and either support or qualify the abstract's 'clear winner' and the conclusion's 'significantly outperforms' accordingly.
  2. [Section IV-B] The entire frequency-domain comparison uses one feature/classifier pipeline—an AudioSet-pretrained Audio Spectrogram Transformer operating on 128-bin log-mel spectrograms—applied identically to both sensors' signals. Because the pretraining distribution and the mel filterbank are designed for human audio, the head-to-head accuracies may reflect classifier/feature fit rather than sensor physics; the bias could plausibly run in either direction, since the microphone receives an initialization advantage from audio-native pretraining while the laser's interferometric fringe signatures may be poorly represented by mel features, or, conversely, the mel smoothing may discard microphone-relevant fine structure. The authors' own Section V statement that 'microphone spectrograms are more suited towards distinguishing different events' makes this dependence explicit. Because the conclusions in Section V are about the sensors' noise resilience rather than about this particular classifier, the authors should add a robustness check with a sensor-matched or feature-agnostic control—for example, training the same architecture from scratch per sensor on the same features, or a second representation such as a raw-waveform CNN or fringe-rate spectral features for SMI—and show that the ranking and per-scenario accuracies are stable; otherwise the central comparison is confounded with the feature pipeline.
  3. [Section III-B, Eqs. (1)-(2)] The time-domain SNR comparisons—laser better by 13.5 dB under white noise, and 4.8 vs 11.2 dB separation with 'perfect separation' for the laser in the bolt experiment—are reported as single point estimates. The number of drops per condition (M), the number of independent measurement sessions, and any run-to-run variability are not given, and no confidence intervals or error bars appear in Fig. 4(c)-(e). These values are used in Section V as supporting evidence for the frequency-domain interpretation, so the comparative claims in this section cannot currently be assessed for precision; the authors should provide repetition statistics or clearly label the figures as single-session measurements.
minor comments (7)
  1. [Section IV-B] The paper states that the model is trained with a 'binary cross-entropy loss' for a three-class task; please specify whether categorical cross-entropy or multi-label binary cross-entropy was used.
  2. [Section IV-A] The paper should state explicitly that in the four targeted test sets the robot-held cup is always empty (the disturbance comes from the person-held cup) and that accuracy is scored against the robot cup's true content; this is implied but never stated, and it is essential for interpreting the confusion matrices.
  3. [Section V] The explanation for the laser's 0.86 on 'Playdough, mimicking robot' invokes 'an accidental, uncontrolled disturbance or the non-stationary noise floor'; since the non-stationary noise floor is a known property from [8], it should be treated as a quantifiable sensor limitation (e.g., measured during the test session) rather than an ad-hoc explanation.
  4. [Eq. (2)] Equation (2) does not define p_i; clarify that these are peak-normalized event amplitudes and specify how peaks are detected and over what window the noise power P_noise is estimated.
  5. [References] Reference [8] has an inconsistent citation year ('in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2025'); the author name 'L. V. den Stockt' likely should be 'L. Van den Stockt'.
  6. [Fig. 7] Fig. 7 should label both axes with units (time, frequency) and state the color-map normalization per panel; as printed, the contrast between the microphone and laser spectrograms is difficult to compare quantitatively.
  7. [Section V] The sentence 'microphone spectrograms are more suited towards distinguishing different events' reads as contradicting the accuracy tables; rephrase to state the two-sided conclusion explicitly (the microphone's spectrograms carry more event-discriminative content but also more disturbance content, which is why its accuracy degrades when disturbances are present).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SMI-versus-microphone comparison rests on direct measurements, not on fitted parameters or self-cited theorems.

full rationale

The paper's central claims are empirical measurements, not derived predictions. Sensor outputs are recorded directly, per-sensor classifiers are trained on a clean training set, and accuracies are reported on held-out disturbance test sets. No quantity asserted as a prediction is computed from a fitted parameter or from an equation that presupposes the conclusion. The only self-citations ([8]) are used for hardware design, prior time-domain results, and a possible non-stationary noise floor explanation for one anomaly; none of these force the frequency-domain ranking. The use of the Audio Spectrogram Transformer with audio-derived mel-spectrogram preprocessing is a benchmark choice rather than a circular reduction; the paper explicitly acknowledges in Section V that 'microphone spectrograms are more suited towards distinguishing different events,' which is an admitted limitation of the comparison, not a hidden identification of input with output. No uniqueness theorem, no ansatz smuggled in via citation, and no renamed empirical pattern appear in the derivation chain. Therefore, under the required standard of exhibiting a specific reduction, no circular step is present.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper is an experimental comparison, so the ledger is light. One measurement-instrument dependency is the fitted AST classifier used for evaluation, and the chosen spectrogram parameters. A few domain assumptions anchor the comparison: the SMI signal model, the equivalence of the two sensors as sound/vibration sensors, and simultaneous exposure. No new physical entities are introduced.

free parameters (2)
  • AST classifier weights (pretrained on AudioSet, fine-tuned on 150 trials) = not disclosed
    The reported accuracy scores are outputs of this fitted classifier; the weights are not released, so the exact numbers depend on an unprovided fitted model.
  • Spectrogram preprocessing parameters (128 Mel bins, 25 ms window, 10 ms hop) = chosen from AST [9]
    These hand-chosen parameters are applied identically to both sensors and can affect how well each sensor's distinguishing features survive the representation, particularly for the frequency-spread SMI signal.
assumptions (4)
  • domain assumption SMI signal model of Plantier et al. [2], in which each lambda/2 target displacement produces a fringe in the photodiode current.
    Invoked in Section I and Fig. 1 to interpret SMI output. The experimental conclusions do not depend on the precise model, but a different transduction mechanism would change what the sensor measures.
  • domain assumption Acoustic and SMI signals are treated as equivalent measurements of mechanically coupled sound waves.
    Section I states microvibrations 'are nothing but mechanically coupled sound waves,' justifying the direct comparison of the two sensors for contact detection.
  • domain assumption AudioSet-pretrained AST transfers sufficiently well to both sensor modalities after identical preprocessing.
    Section IV-B uses the same transformer and spectrogram parameters for microphone and laser; if the pretrained model is better matched to audio, the accuracy comparison is biased.
  • domain assumption The sensors are exposed simultaneously to identical physical disturbances, so per-trial paired comparisons are valid.
    Section IV-A states SMI and acoustic data are recorded simultaneously but gives no synchronization or cross-talk analysis.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Self-Mixing Laser Interferometry: In Search of an Ambient Noise-Resilient Alternative to Acoustic Sensing." pith.science (2026). https://pith.science/paper/ZEPIBIKX

@misc{pith2026250413711,
  author       = {Pith},
  title        = {Pith review of: Self-Mixing Laser Interferometry: In Search of an Ambient Noise-Resilient Alternative to Acoustic Sensing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZEPIBIKX}},
  note         = {Machine review of arXiv:2504.13711}
}
read the original abstract

Self-mixing interferometry (SMI) has been lauded for its sensitivity in detecting microvibrations, while requiring no physical contact with its target. Microvibrations, i.e., sounds, have recently been used as a salient indicator of extrinsic contact in robotic manipulation. In previous work, we presented a robotic fingertip using SMI for extrinsic contact sensing as an ambient-noise-resilient alternative to acoustic sensing. Here, we extend the validation experiments to the frequency domain. We find that for broadband ambient noise, SMI still outperforms acoustic sensing, but the difference is less pronounced than in time-domain analyses. For targeted noise disturbances, analogous to multiple robots simultaneously collecting data for the same task, SMI is still the clear winner. Lastly, we show how motor noise affects SMI sensing more so than acoustic sensing, and that a higher SMI readout frequency is important for future work. Design and data files are available at https://github.com/RemkoPr/icra2025-SMI-tactile-sensing.

Figures

Figures reproduced from arXiv: 2504.13711 by the authors.

Figure 1
Figure 1. Self-mixing interferometry. Every time the target [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. SMI readout circuit. (a) Fingertips mounted on a Robo￾tiq 2F-85 gripper. (b) Section views [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Mechanical design of the fingertips. A. Circuit Design 1) SMI sensor: A schematic representation of the SMI cir￾cuit is shown in [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Measuring extrinsic contact of objects falling into a [PITH_FULL_IMAGE:figures/full_fig_p002_4.png]
Figure 5
Figure 5. Figure 5: Setup for the cup shaking experiment. Either three [PITH_FULL_IMAGE:figures/full_fig_p003_5.png]
Figure 7
Figure 7. Figure 7: Motor noise at different turning speeds of the robot [PITH_FULL_IMAGE:figures/full_fig_p004_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

12 extracted references · 8 canonical work pages

  1. [8]

    Z. Liu, C. Chi, E. Cousineau, N. Kuppuswamy, B. Burchfiel, and S. Song, ``Maniwav: Learning robot manipulation from in-the-wild audio-visual data,'' arXiv preprint arXiv:2406.19464, 2024

    arXiv 2024

  2. [1]

    u RSS # , p p p p p 텅),,Lݻ8 1 `?4M

    11em plus .33em minus .07em @technote 4000 4000 100 4000 4000 500 `\.=1000 = #1 #1 #1 0pt [0pt][0pt] #1 * \| ** #1 \@IEEEauthorblockNstyle \@IEEEauthorblockAstyle \@IEEEauthordefaulttextstyle \@IEEEauthorblockconfadjspace -0.25em \@IEEEauthorblockNtopspace 0.0ex \@IEEEauthorblockAtopspace 0.0ex \@IEEEauthorblockNinterlinespace 2.6ex \@IEEEauthorblockAinte...

    arXiv 1928

  3. [2]

    Y. Yu, Y. Fan, and B. Liu, `` Self-mixing interferometry and its applications ,'' in Optical Design and Testing VII, Y. Wang, T. E. Kidger, and K. Tatsuno, Eds., vol. 10021, International Society for Optics and Photonics. 1em plus 0.5em minus 0.4em SPIE, 2016, p. 100210U

    work page 2016

  4. [3]

    Plantier, C

    G. Plantier, C. Bes, and T. Bosch, ``Behavioral model of a self-mixing laser diode sensor,'' IEEE Journal of Quantum Electronics, vol. 41, no. 9, pp. 1157--1167, 2005

    work page 2005

  5. [4]

    Donati and M

    S. Donati and M. Norgia, `` Overview of self-mixing interferometer applications to mechanical engineering ,'' Optical Engineering, vol. 57, no. 5, p. 051506, 2018

    work page 2018

  6. [5]

    ------, ``Self-mixing interferometry for biomedical signals sensing,'' IEEE Journal of Selected Topics in Quantum Electronics, vol. 20, no. 2, pp. 104--111, 2014

    work page 2014

  7. [6]

    Kyberd, ``Slip detection strategies for automatic grasping in prosthetic hands,'' Sensors, vol

    P. Kyberd, ``Slip detection strategies for automatic grasping in prosthetic hands,'' Sensors, vol. 23, no. 9, 2023

    work page 2023

  8. [7]

    R. A. Romeo and L. Zollo, ``Methods and sensors for slip detection in robotics: A survey,'' IEEE Access, vol. 8, pp. 73\,027--73\,050, 2020

    work page 2020

Show all 12 references
  1. [9]

    Proesmans, W

    R. Proesmans, W. Goossens, L. V. den Stockt, L. Christiaen, and F. wyffels, ``Self-mixing laser interferometry for robotic tactile sensing,'' in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2025

    2023

  2. [10]

    Gong, Y.-A

    Y. Gong, Y.-A. Chung, and J. Glass, ``Ast: Audio spectrogram transformer,'' arXiv preprint arXiv:2104.01778, 2021

    2021 arXiv

  3. [11]

    Touvron, M

    H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. J \'e gou, ``Training data-efficient image transformers & distillation through attention,'' in International conference on machine learning. 1em plus 0.5em minus 0.4em PMLR, 2021, pp. 10\,347--10\,357

    2021

  4. [12]

    J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, ``Audio set: An ontology and human-labeled dataset for audio events,'' in 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). 1em plus 0....

    2017

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.