Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning.

ABSTRACT: Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and "unsupervised", meaning it requires no manual data labelling, yet it can i

SUBMITTER: Stowell D 

PROVIDER: S-EPMC4106198 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature