  Acoustic scene recordings are represented by different types of handcrafted
or Neural Network-derived features. These features, typically of thousands of
dimensions, are classified in state of the art approaches using kernel
machines, such as the Support Vector Machines (SVM). However, the complexity of
training these methods increases with the dimensionality of these input
features and the size of the dataset. A solution is to map the input features
to a randomized lower-dimensional feature space. The resulting random features
can approximate non-linear kernels with faster linear kernel computation. In
this work, we computed a set of 6,553 input features and used them to compute
random features to approximate three types of kernels, Gaussian, Laplacian and
Cauchy. We compared their performance using an SVM in the context of the DCASE
Task 1 - Acoustic Scene Classification. Experiments show that both, input and
random features outperformed the DCASE baseline by an absolute 4%. Moreover,
the random features reduced the dimensionality of the input by more than three
times with minimal loss of performance and by more than six times and still
outperformed the baseline. Hence, random features could be employed by state of
the art approaches to compute low-storage features and perform faster kernel
computations.
