Comment by antegamisou

This is hardly a new concept btw.

In 2005 ACM's CCS Zhuang, Zhou and Tygar presented Keyboard Acoustic Emanations Revisited [1]

    We examine the problem of keyboard acoustic emanations. We
    present a novel attack taking as input a 10-minute sound recording
    of a user typing English text using a keyboard, and then recovering 
    up to 96% of typed characters. There is no need for a labeled
    training recording. Moreover the recognizer bootstrapped this way
    can even recognize random text such as passwords: In our experiments, 
    90% of 5-character random passwords using only letters can
    be generated in fewer than 20 attempts by an adversary; 80% of 10-
    character passwords can be generated in fewer than 75 attempts.
    Our attack uses the statistical constraints of the underlying content, 
    English language, to reconstruct text from sound recordings
    without any labeled training data. The attack uses a combination
    of standard machine learning and speech recognition techniques,
    including cepstrum features, Hidden Markov Models, linear classification, 
    and feedback-based incremental learning

which builds up on Asonov & Agrawal's work [2] who came up with the idea the previous year (2004).

    We show that PC keyboards, notebook keyboards, telephone 
    and ATM pads are vulnerable to attacks based on
    differentiating the sound emanated by different keys. Our
    attack employs a neural network to recognize the key being 
    pressed. We also investigate why different keys produce
    different sounds and provide hints for the design of homophonic 
    keyboards that would be resistant to this type of attack.

[1] https://dl.acm.org/doi/10.1145/1609956.1609959

[2] https://ieeexplore.ieee.org/document/1301311