  Speech audio quality is subject to degradation caused by an acoustic
environment and isotropic ambient and point noises. The environment can lead to
decreased speech intelligibility and loss of focus and attention by the
listener. Basic acoustic parameters that characterize the environment well are
(i) signal-to-noise ratio (SNR), (ii) speech transmission index, (iii)
reverberation time, (iv) clarity, and (v) direct-to-reverberant ratio. Except
for the SNR, these parameters are usually derived from the Room Impulse
Response (RIR) measurements; however, such measurements are often not
available. This work presents a universal room acoustic estimator design based
on convolutional recurrent neural networks that estimate the acoustic
environment measurement blindly and jointly. Our results indicate that the
proposed system is robust to non-stationary signal variations and outperforms
current state-of-the-art methods.
