Classification of bird species by using key song searching: a comparative study
To better understand the environment we are living, especially animals and birds around us, we need to study their behavior and the way of their communication. This paper addresses the problem of classifying bird species of interests using the digital signals of recorded bird songs. First, through comparisons of speech and bird song signals and the experiments, we propose a simple model (similar to that of the speech) for generating synthetic bird songs. We then propose a key-bird-song searching method for the recognition of bird species of interest. This is possible since bird songs appear to be simpler as compared to human speech. A hierarchical classification method is then suggested. In the coarse level of the classification, only candidate songs from those birds whose time-dependent coupled sound patterns are 'close' to that of the species of interest are chosen as the candidates. In the fine level, time-frequency 'format' trajectory-related features from the candidate songs are used for the classification. A case study is conducted for the recognition of a selected bird species, the Great Tit. Preliminary experimental results from using five different bird species and 87 songs have shown promising results in recognizing the selected bird species of interest, with less than 3% of classification errors.