  This paper investigates the application of environmental feature
representations for room verification tasks and acoustic meta-data estimation.
Audio recordings contain both speaker and non-speaker information. We refer to
the non-speaker-related information, including channel and other environmental
factors, as e-vectors. I-vectors, commonly used in speaker identification, are
extracted in the total variability space and capture both speaker and
channel-environment information without discrimination. Accordingly, e-vectors
can be extracted from i-vectors using methods such as linear discriminant
analysis. In this paper, we first demonstrate that e-vectors can be
successfully applied to room verification tasks with a low equal error rate.
Second, we propose two methods for estimating metadata information --
signal-to-noise (SNR) and reverberation (T60) -- from these e-vectors. When
comparing our system to contemporary global SNR estimation methods, in terms of
accuracy, we perform favorably even with low dimensional i-vectors. Lastly, we
show that room verification tasks can be improved if e-vectors are augmented
with the extracted metadata information.
