Thanks
Mark and
Laurent for the heads-up on finding this work.
DCASE 2017 TASK 1: Acoustic Scene Classification Using Shift-Invariant Kernels and Random Features by Abelino Jimenez, Benjamın Elizalde and Bhiksha Raj
Acoustic scene recordings are represented by different types of
handcrafted or Neural Network 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
low-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, Guassian, 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.
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