@article {53580, title = {Contributions of MIR to soundscape ecology. Part 2: Spectral timbral analysis for discriminating soundscape components}, journal = {Ecological Informatics}, year = {2019}, month = {Jan-02-2019}, abstract = {

Soundscape ecology evaluates biodiversity and environmental disturbances by investigating the interaction among soundscape components (biological, geophysical, and human-produced sounds) using data collected with autonomous recording units. Current analyses consider the acoustic properties of frequency and amplitude resulting in varied metrics, but rarely focus on the discrimination of soundscape components. Computational musicologists analyze similar data but consider a third acoustic property, timbre.

Here, we investigated the effectiveness of spectral timbral analysis to distinguish among dominant soundscape components. This process included manually labeling and extracting spectral timbral features for each recording. Then, we tested classification accuracy with linear and quadratic discriminant analyses on combinations of spectral timbral features.

Different spectral timbral feature groups distinguished between biological, geophysical, and manmade sounds in a single field recording. Furthermore, as we tested different combinations of spectral timbral features that resulted in both high and very low accuracy results, we found that they could be ordered to \“sift\” out field recordings by individual dominant soundscape component.

By using timbre as a new acoustic property in soundscape analyses, we could classify dominant soundscape components effectively. We propose further investigation into a sifting scheme that may allow researchers to focus on more specific research questions such as understanding changes in biodiversity, discriminating by taxonomic class, or to inspect weather-related events.

}, issn = {15749541}, doi = {10.1016/j.ecoinf.2019.01.008}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118301900}, author = {VanSchaik, Jack and Zhao, Zhao and Gasc, Amandine and Omrani, Hichem and Bryan C. Pijanowski} } @article {53445, title = {Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys}, journal = {Ecological Informatics}, volume = {49}, year = {2019}, month = {Jan-01-2019}, pages = {45 - 53}, abstract = {

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of \>650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85\%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (\>90\%). Adopting a desired accuracy probability threshold of 95\% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75\%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.

}, issn = {15749541}, doi = {10.1016/j.ecoinf.2018.11.004}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118300232https://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/plain}, author = {L{\'o}pez-Baucells, Adri{\`a} and Torrent, Laura and Rocha, Ricardo and Paulo E.D. Bobrowiec} } @article {53351, title = {Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys}, journal = {Ecological Informatics}, year = {2018}, month = {Jan-11-2018}, abstract = {

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of \>650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85\%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (\>90\%). Adopting a desired accuracy probability threshold of 95\% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75\%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.

}, keywords = {Amazon, bioacoustics, Chiroptera, echolocation, Machine-learning algorithms}, issn = {15749541}, doi = {10.1016/j.ecoinf.2018.11.004}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118300232https://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/plain}, author = {L{\'o}pez-Baucells, Adri{\`a} and Torrent, Laura and Rocha, Ricardo and Paulo E.D. Bobrowiec and Jorge M. Palmeirim and Christoph F.J. Meyer} } @article {47352, title = {Automated bird acoustic event detection and robust species classification}, journal = {Ecological Informatics}, year = {2017}, month = {Jan-04-2017}, abstract = {

N on - invasive b ioacoustic monitoring is becoming increasingly popular for b iodiversity conservation. Two automated methods for acoustic classification of bird species currently used are frame - based methods, a model that uses Hidden Markov Models (HMMs), and event - based methods, a model consisting of descriptive measurements or restricted to tonal or harmonic vocalizations. In this work, we propose a new method for automated field recording anal ysis with improved automated segm entation and robust bird species classification . We used a Gaussian Mixture Model ( GMM ) - based frame selection with an event - energy - based sifting procedure that selected representative acoustic events . We employed a Mel , band - pass filter bank on each event \’ s spectrogram. T he output in each subband was parameterized by an autoregressive (AR) model , which result ed in a feature consisting of all model coefficients. Finally, a support vector machine (SVM) algorithm was used for classification . The significance o f the proposed method lies in the parameterized feature s depict ing the species - specific spectral pattern . This experiment used a control audio dataset and real - world audio dataset comprised of field recordings of eleven bird species from the X eno - canto A rc hive , consisting of 2762 bird acoustic events with 339 detected \“ unknown \” events (corresponding to noise or unknown species vocalization s) . Compared with other recent approach es , our proposed method provide s comparable identification performance with respe ct to the eleven species of interest . Meanwhile, superior robu stness in real - world scenarios is achieved, which is expressed as the considerable improvement from 0.632 to 0.928 for the F - score metric regarding the \“ unknown \” events . The advantage makes the proposed method more suitable for automated field recording analysis.

}, keywords = {automated acoustic event detection, autoregressive model, bioacoustic monitoring, Gaussian mixture model, robust bird species classification}, issn = {15749541}, doi = {10.1016/j.ecoinf.2017.04.003}, url = {http://linkinghub.elsevier.com/retrieve/pii/S157495411630231X}, author = {Zhao, Zhao and Zhang, Sai-hua and Xu, Zhi-yong and Kristen M. Bellisario and Dai, Nian-hua and Omrani, Hichem and Bryan C. Pijanowski} } @article {16647, title = {Management of acoustic metadata for bioacoustics}, journal = {Ecological Informatics}, volume = {31}, year = {2016}, month = {Jan-01-2016}, pages = {122 - 136}, issn = {15749541}, doi = {10.1016/j.ecoinf.2015.12.002}, url = {http://linkinghub.elsevier.com/retrieve/pii/S1574954115001983}, author = {Roch, Marie A. and Batchelor, Heidi and Baumann-Pickering, Simone and Catherine L. Berchok and Cholewiak, Danielle and Fujioka, Ei and Garland, Ellen C. and Herbert, Sean and Sofie M. Van Parijs and Risch, Denise and {\v S}irovi{\'c}, Ana and Soldevilla, Melissa S.} } @article {11296, title = {Decision support for the efficient annotation of bioacoustic events}, journal = {Ecological Informatics}, volume = {25}, year = {2014}, pages = {14-21}, chapter = {14}, abstract = {

Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories.

We describe two algorithms: (1) a {\textquotedblleft}naìˆve{\textquotedblright} decision support tool (16\%{\textendash}64\% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48\% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacous- tics projects.

}, keywords = {annotations, bioacoustics, decision support, Faunal vocalisation, semi-automated, Similarity search}, doi = {10.1016/j.ecoinf.2014.10.001}, author = {Anthony Truskinger and Paul Roe} }