  We present an iVector based Acoustic Scene Classification (ASC) system suited
for real life settings where active foreground speech can be present. In the
proposed system, each recording is represented by a fixed-length iVector that
models the recording's important properties. A regularized Gaussian backend
classifier with class-specific covariance models is used to extract the
relevant acoustic scene information from these iVectors. To alleviate the large
performance degradation when a foreground speaker dominates the captured
signal, we investigate the use of the iVector framework on Mel-Frequency
Cepstral Coefficients (MFCCs) that are derived from an estimate of the noise
power spectral density. This noise-floor can be extracted in a statistical
manner for single channel recordings. We show that the use of noise-floor
features is complementary to multi-condition training in which foreground
speech is added to training signal to reduce the mismatch between training and
testing conditions. Experimental results on the DCASE 2016 Task 1 dataset show
that the noise-floor based features and multi-condition training realize
significant classification accuracy gains of up to more than 25 percentage
points (absolute) in the most adverse conditions. These promising results can
further facilitate the integration of ASC in resource-constrained devices such
as hearables.
