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p.s
Henkjan discussed three types of systems in his previous message and referred to our method as the third type (post processing methods).
In fact our technology is fully realizing what he called type 2 (client side software) as it can be directly used from the browser with no additional hardware or software. The trick is to use signal processing and computer
audio in ways that are explained in the paper: https://www.biorxiv.org/content/10.1101/2021.01.15.426897v2
Very best,
Nori
Nori Jacoby Max Planck Group Leader, “Computational Auditory Perception” Max Planck Institute for Empirical Aesthetics Grüneburgweg 14, 60322 Frankfurt am Main, Germany nori.jacoby@xxxxxxxxx +49 69 8300479-820 From: Jacoby, Nori
Sent: Tuesday, January 26, 2021 5:27:41 PM To: AUDITORY@xxxxxxxxxxxxxxx Subject: Re: [AUDITORY] Online rhythm production experiments: Update
Dear auditory list,
We would like to share this preprint to present and validate a recently developed technology in our group: REPP (Rhythm ExPeriment Platform). This technology is able to measure SMS in online experiments with high temporal fidelity while also working efficiently
using hardware and software available to most participants online. We give details on the technology in the preprint as well as validate it in a series of calibration and behavioral experiments. We demonstrate that our technology achieves high temporal accuracy
(latency and jitter within 2 ms on average) and high test-retest reliability both in the laboratory and online. We plan to release this technology as a free and open-source framework alongside the journal version of the paper.
This technology is fully automated and customizable, enabling researchers to monitor online experiments in real time and to implement a wide variety of SMS paradigms. For example, using REPP we successfully replicated online a transmission chain experiment
to estimate perceptual priors for simple rhythms via iterated reproduction of random temporal sequences. In a recent paper, we also show that REPP can be used to collect a large tapping dataset with more than 500 participants to study individual differences
on SMS in the general population (Niarchou et al., 2021). We therefore believe this technology can support SMS experiments that would be nearly impossible in the lab while massively increasing the scalability and speed of data collection.
https://www.biorxiv.org/content/10.1101/2021.01.15.426897v2
Very best,
Nori Jacoby, Manuel Anglada-Tort, Peter Harrison
Nori Jacoby Max Planck Group Leader, “Computational Auditory Perception” Max Planck Institute for Empirical Aesthetics Grüneburgweg 14, 60322 Frankfurt am Main, Germany nori.jacoby@xxxxxxxxx +49 69 8300479-820 ---------- Forwarded message ---------
From: Prof. dr Henkjan Honing <honing@xxxxxx> Date: Mon, Oct 26, 2020 at 5:24 AM Subject: [AUDITORY] Online rhythm production experiments: Update To: AUDITORY@xxxxxxxxxxxxxxx <AUDITORY@xxxxxxxxxxxxxxx> Thanks for the suggestions. Below a brief summary of the responses I received. These came in three flavors:
1) solutions suggesting
specific hardware at the client side (e.g. using e.g., a two channel audio card)
2) solutions using
client side software (e.g., _javascript_)
3) offline and/or
post-processing solutions
For our purpose (relatively large-scale online rhythm production experiments)
solution type 1 is unrealistic.
[input from Werner Hemmert and others]
Solution type 2 was tried by several researchers/institutes (using, e.g, PsychoPY _javascript_, etc.). However, most report - as expected - relatively large timing errors, largely due to keyboard
scan rates, drivers, and/or operating system (as reported in the references mentioned in the original message). (Despite the claim of
psychopy.org of <4ms precision in online studies).
[Input from Ignacio Spiousas, Nick Haywood, Ben Schultz, Kyle Jasmin and others]
N.B. PeerJ recently published a comparative study [1]
Solution type 3 was suggested by some: i.e. o record the rhythmic pattern by tapping e.g. with a pencil on your desk or device microphone, along with the streamed sound, at the client side,
upload the resulting audio file using a standard browser, and analyse it at the serverside using onset-detection and some crosscorrelation techniques. Depending on the sampling rate, latencies can be reduced to 1 ms or less.
[Input from Roger Dannenberg, Krzysztof Basiński, Justin London and others]
N.B.1 Ben Schultz announced to make his version of Solution type 1 available as open source (repeated below).
N.B.2. Nori Jacoby announced to make their version of Solution type 3 available as appendix to a forthcoming paper (repreated below).
Nevertheless, my hope is still on some elegant solution of type 2. If you have one, please let us know.
Best,
Henkjan Honing
——
Nori Jacoby
Max Planck Group Leader, “Computational Auditory Perception” Max Planck Institute for Empirical Aesthetics Grüneburgweg 14, 60322 Frankfurt am Main, Germany. |