Large-scale analysis of frequency modulation in birdsong data bases

Birdsong often contains large amounts of rapid frequency modulation (FM). It is believed that the use or otherwise of FM is adaptive to the acoustic environment and also that there are specific social uses of FM such as trills in aggressive territorial encounters. Yet temporal fine detail of FM is often absent or obscured in standard audio signal analysis methods such as Fourier analysis or linear prediction. Hence, it is important to consider high-resolution signal processing techniques for analysis of FM in bird vocalizations. If such methods can be applied at big data scales, this offers a further advantage as large data sets become available. We introduce methods from the signal processing literature which can go beyond spectrogram representations to analyse the fine modulations present in a signal at very short time-scales. Focusing primarily on the genus Phylloscopus, we investigate which of a set of four analysis methods most strongly captures the species signal encoded in birdsong. We evaluate this through a feature selection technique and an automatic classification experiment. In order to find tools useful in practical analysis of large data bases, we also study the computational time taken by the methods, and their robustness to additive noise and MP3 compression. We find three methods which can robustly represent species-correlated FM attributes and can be applied to large data sets, and that the simplest method tested also appears to perform the best. We find that features representing the extremes of FM encode species identity supplementary to that captured in frequency features, whereas bandwidth features do not encode additional information. FM analysis can extract information useful for bioacoustic studies, in addition to measures more commonly used to characterize vocalizations. Further, it can be applied efficiently across very large data sets and archives.