@article {53257, title = {Exploring the biodiversity in Alas Purwo National Park, East Java through soundscape ecology}, journal = {Journal of Physics: Conference Series}, volume = {1075}, year = {2018}, month = {Jan-08-2018}, pages = {012003}, abstract = {

Biodiversity assessment is becoming one important things to do, to conserve ecosystem. Biodiversity assessment\&$\#$39;s new method was utilized the nature sounds which is produced by animals in an ecosystem. There are three possibilities sounds sources in an ecosystem, they are: biophonies, geophonies, and anthrophonies, those sound sources will reflect environment acoustics characteristics, called Soundscape. This paper utilized the Soundscape method to predict biodiversity in a forest. Alas Purwo National Park, Banyuwangi, East Java was the location where soundscape data were collected for several days. The soundscape data was processed using R-Statistical Computing Environment software to compute some index values of Bioacoustics Index (BIO), Acoustic Entropy (H), Acoustic Evenness Index (AEI) and Normalized Difference Soundscape Index (NDSI). These indices enabled to characterize the forest in Alas Purwo National Park, East Java, Indonesia for several types of forests selected. The AEI value of 0.69 indicate inequality between frequency bands. The BIO value of 10.79 indicate a forest that is dominant of biotic component. The H value of 0.88 indicate more diverse biotic components. The NDSI value of 0.97 indicate a forest that is dominant with biophonies rather than antrophonies. The results have indicated that some indices (AEI and BIO) could reflect the biodiversity in selected ecosystem, but other indices (H and NDSI) showed anomaly result because of difference landscape between selected ecosystem and ecosystem\&$\#$39;s weather when data collecting period.

}, issn = {1742-6588}, doi = {10.1088/1742-6596/1075/1/012003}, url = {http://stacks.iop.org/1742-6596/1075/i=1/a=012003?key=crossref.aa7303cff3940be910453ceb9a3a6311}, author = {Setyantho, G R and Utami, S S and Hadi, S} } @article {50905, title = {Frog sound identification using extended k-nearest neighbor classifier}, journal = {Journal of Physics: Conference Series}, volume = {890}, year = {2017}, month = {Jan-09-2017}, pages = {012070}, abstract = {

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

}, issn = {1742-6588}, doi = {10.1088/1742-6596/890/1/012070}, url = {http://stacks.iop.org/1742-6596/890/i=1/a=012070?key=crossref.393dd3e387b6a34eb3971240d516f44b}, author = {Mukahar, Nordiana and Rosdi, Bakhtiar Affendi and Ramli, Dzati Athiar and Jaafar, Haryati} }