Search for: All records

We explore computational strategies for matching human vocal imitations of birdsong to actual birdsong recordings. We recorded human vocal imitations of birdsong and subsequently analysed these data using three categories of audio features for matching imitations to original birdsong: spectral, temporal, and spectrotemporal. These exploratory analyses suggest that spectral features can help distinguish imitation strategies (e.g. whistling vs. singing) but are insufficient for distinguishing species. Similarly, whereas temporal features are correlated between human imitations and natural birdsong, they are also insufficient. Spectrotemporal features showed the greatest promise, in particular when used to extract a representation of the pitch contour of birdsong and human imitations. This finding suggests a link between the task of matching human imitations to birdsong to retrieval tasks in the music domain such as query-by-humming and cover song retrieval; we borrow from such existing methodologies to outline directions for future research. 

This paper proposes to perform unsupervised detection of bioacous- tic events by pooling the magnitudes of spectrogram frames after per-channel energy normalization (PCEN). Although PCEN was originally developed for speech recognition, it also has beneficial effects in enhancing animal vocalizations, despite the presence of atmospheric absorption and intermittent noise. We prove that PCEN generalizes logarithm-based spectral flux, yet with a tunable time scale for background noise estimation. In comparison with point- wise logarithm, PCEN reduces false alarm rate by 50x in the near field and 5x in the far field, both on avian and marine bioacoustic datasets. Such improvements come at moderate computational cost and require no human intervention, thus heralding a promising future for PCEN in bioacoustics.