Rapid bird activity detection using probabilistic sequence kernels

Abstract

Bird activity detection is the task of determining if a bird sound is present in a given audio recording. This paper describes a bird activity detector which utilises a support vector machine (SVM) with a dynamic kernel. Dynamic kernels are used to process sets of feature vectors having different cardinalities. Probabilistic sequence kernel (PSK) is one such dynamic kernel. The PSK converts a set of feature vectors from a recording into a fixed-length vector. We propose to use a variant of PSK in this work. Before computing the fixed-length vector, cepstral mean and variance normalisation and short-time Gaussianization is performed on the feature vectors. This reduces environment mismatch between different recordings. Additionally, we also demonstrate a simple procedure to speed up the proposed method by reducing the size of fixed-length vector. A speedup of almost 70% is observed, with a very small drop in accuracy. The proposed method is also compared with a random forest classifier and is shown to outperform it.