  In Acoustic Scene Classification (ASC) two major approaches have been
followed . While one utilizes engineered features such as
mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features
that are the outcome of an optimization algorithm. I-vectors are the result of
a modeling technique that usually takes engineered features as input. It has
been shown that standard MFCCs extracted from monaural audio signals lead to
i-vectors that exhibit poor performance, especially on indoor acoustic scenes.
At the same time, Convolutional Neural Networks (CNNs) are well known for their
ability to learn features by optimizing their filters. They have been applied
on ASC and have shown promising results. In this paper, we first propose a
novel multi-channel i-vector extraction and scoring scheme for ASC, improving
their performance on indoor and outdoor scenes. Second, we propose a CNN
architecture that achieves promising ASC results. Further, we show that
i-vectors and CNNs capture complementary information from acoustic scenes.
Finally, we propose a hybrid system for ASC using multi-channel i-vectors and
CNNs by utilizing a score fusion technique. Using our method, we participated
in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st
rank among 49 submissions, substantially improving the previous state of the
art.
