Acoustic Scene Classification Using Discrete Random Hashing for Laplacian Kernel Machines

Abstract

State of the art acoustic scene classification techniques often employ features of large dimensionality, which are then used to train and perform inferences with kernel machines such as Support Vector Machines. However, the complexity of computing the non-linear kernel matrix for these methods increases with the dimensionality of the features and the size of the dataset. In this work, we introduce a new scheme that hashes features, which combined with a linear function approximates a non-linear Laplacian kernel. Each hash typically has lower dimensionality than the input features and each component is represented by one bit instead of floating values. Hence, allowing efficient computation of the kernel matrix using XOR operations rather than dot-products. Our scheme is demonstrated mathematically and tested in the 2017 DCASE: Acoustic Scene Classification. The hashes reduce up to six powers of two the feature representation with minimal loss of accuracy.