Spectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection

Publication Type:Journal Article
Year of Publication:2017
Authors:Koluguri, G. Meenakshi, Ghosh
Journal:IEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume:25
Questão:6
Pagination:1183 - 1192
Date Published:Jan-06-2017
ISSN:2329-9290
Palavras-chave:bioacoustic monitoring, bird sound detection, birds, directional spectrograms, hidden Markov models, monitoring, noise measurement, robustness, Savitzky-Golay filter, spectrogram
Abstract:

Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky–Golay (MWSG) filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time–frequency (T–F) plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score (10.24% relative) compared to the best of the six baseline schemes considered in this work.

URL:http://ieeexplore.ieee.org/document/7933047/
DOI:10.1109/TASLP.2017.2690562
Short Title:IEEE/ACM Trans. Audio Speech Lang. Process.
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Scratchpads developed and conceived by (alphabetical): Ed Baker, Katherine Bouton Alice Heaton Dimitris Koureas, Laurence Livermore, Dave Roberts, Simon Rycroft, Ben Scott, Vince Smith