02624nas a2200289 4500008004100000022001400041245011100055210006900166260001600235300001600251490000700267520172500274653002701999653002702026653001002053653002902063653002502092653001502117653002202132653001502154653002602169653001602195100002602211700002102237700002702258856004902285 2017 eng d a2329-929000aSpectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection0 aSpectrogram Enhancement Using Multiple Window SavitzkyGolay MWSG cJan-06-2017 a1183 - 11920 v253 a

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.

10abioacoustic monitoring10abird sound detection10abirds10adirectional spectrograms10ahidden Markov models10amonitoring10anoise measurement10arobustness10aSavitzky-Golay filter10aspectrogram1 aKoluguri, Nithin, Rao1 aMeenakshi, Nisha1 aGhosh, Prasanta, Kumar uhttp://ieeexplore.ieee.org/document/7933047/02348nas a2200265 4500008004100000245008600041210006900127260001600196300001400212490000600226520155300232653001701785653002501802653001501827653001201842100001901854700002301873700002201896700002101918700002301939700002201962700002401984700002102008856005302029 2017 eng d00aMatlabHTK: a simple interface for bioacoustic analyses using hidden Markov models0 aMatlabHTK a simple interface for bioacoustic analyses using hidd cJan-05-2017 a615 - 6210 v83 a

1.Passive bioacoustic recording devices are now widely available and able to continuously record remotely located sites for extended periods, offering great potential for wildlife monitoring and management. Analysis of the huge data sets generated, in particular for specific biotic sound recognition, remains a critical bottleneck for widespread adoption of these technologies as current methods are labour intensive.

2. Several methods borrowed from speech processing frameworks, such as hidden Markov models, have been successful in analysing bioacoustic data, but the software implementations can be expensive and difficult to use for non-specialists involved in wildlife conservation. To remedy this, we present a software interface to a popular speech recognition system making it possible for non-experts to implement hidden Markov models for bioacoustic signal processing. Octave/Matlab functions are used to simplify the set-up and the definition of a bioacoustic signal recogniser as well as the analysis of the results.

3. We present the different functions as a workflow. To demonstrate how the package can be used, we give the results of an analysis of a bioacoustic monitoring data set to detect the nocturnal presence and behaviour of a cryptic seabird species, the common diving petrel Pelecanoides urinatrix urinatrix, from Northern New Zealand.


4. We show that the package MatlabHTK can be used efficiently to reconstruct the daily patterns of colony activity in the common diving petrel.

10abioacoustics10ahidden Markov models10amonitoring10aseabird1 aRanjard, Louis1 aReed, Benjamin, S.1 aLanders, Todd, J.1 aRayner, Matt, J.1 aFriesen, Megan, R.1 aSagar, Rachel, L.1 aDunphy, Brendon, J.1 aGaggiotti, Oscar uhttp://doi.wiley.com/10.1111/mee3.2017.8.issue-5