Compressed Convex Spectral Embedding for Bird Species Classification

Abstract

This paper focuses on the problem of bird species identification using audio recordings. Following recent developments in deep learning, we propose a multi-layer alternating sparse-dense framework for bird species identification. Temporal and frequency modulations in bird vocalizations are captured by concatenating frames of spectrograms, resulting in a high dimensional super-frame based representation. These super-frame representations are highly sparse. Hence, we propose to use random projections to compress these super-frames. This is followed by class-specific archetypal analysis, employed on these compressed super-frames for acoustic modeling, to obtain a convex-sparse representation. These convex-sparse representations are referred as compressed convex spectral embeddings (CCSE). It is observed that these representations efficiently capture species-specific discriminative information. Experimental results show compelling evidence that the proposed approach shows performance comparable to existing methods such as deep neural networks (DNN) and dynamic kernel based SVMs.