The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

  1. Zhao Ren
  2. Yi Chang
  3. Katrin D. Bartl-Pokorny
  4. Florian B. Pokorny
  5. Björn W. Schuller

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Abstract

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  1. Version published to 10.1016/j.jvoice.2022.06.011
  2. ScreenIT

    SciScore for 10.1101/2022.03.01.22271693: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The ComParE features have shown effectiveness in various audio classification tasks, including pathological-speech-related disease detection (Cummins et al., 2020), on small to medium-sized datasets and represent one of the official machine learning pipelines of the INTERSPEECH ComParE series (B.
    ComParE
    suggested: (COMPARE, RRID:SCR_000855)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: The classification performance reported in our study needs to be interpreted in the light of the well-known challenges of data collection via crowdsourcing, including data validity, data quality, and participant selection bias (Afshinnekoo et al., 2016; Khare et al., 2015; Porter et al., 2020). The COUGHVID database does not allow to verify the COVID-19 status of the participants, as the participants were not asked to provide a copy or confirmation of their positive or negative COVID-19 test. Another limitation is that the participants have not been instructed to record the data during a defined time window after the positive or negative COVID-19 test. Therefore, it is possible that some participants recorded their cough at the beginning of their infection, whereas others did the recording towards the end of their infection. Interestingly, the disease stage of COVID-19 was found to influence the nature of the cough (shifting from dry at an early disease stage to more wet at a later disease stage), concomitantly affecting acoustic parameters of the cough (Cohen-McFarlane et al., 2020). Moreover, the participants were asked to answer whether they had respiratory and/or muscle/pain symptoms, but no information on the severity of their symptoms is available. Although the safe recording instructions provided on the web page are reasonable with regard to the transmission of the virus, the suggestion to put the smartphone into a plastic zip bag while recording is subopt...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  3. Version published to 10.1101/2022.03.01.22271693v1 on medRxiv