  Acoustic environment characterization opens doors for sound reproduction
innovations, smart EQing, speech enhancement, hearing aids, and forensics.
Reverberation time, clarity, and direct-to-reverberant ratio are acoustic
parameters that have been defined to describe reverberant environments. They
are closely related to speech intelligibility and sound quality. As explained
in the ISO3382 standard, they can be derived from a room measurement called the
Room Impulse Response (RIR). However, measuring RIRs requires specific
equipment and intrusive sound to be played. The recent audio combined with
machine learning suggests that one could estimate those parameters blindly
using speech or music signals. We follow these advances and propose a robust
end-to-end method to achieve blind joint acoustic parameter estimation using
speech and/or music signals. Our results indicate that convolutional recurrent
neural networks perform best for this task, and including music in training
also helps improve inference from speech.
