Visualization and categorization of ecological acoustic events based on discriminant features

Hilasaca, Liz Maribel Huancapaza; Gaspar, Lucas Pacciullio Da Silva; Ribeiro, Milton Cézar; Minghim, Rosane

doi:10.1016/j.ecolind.2020.107316

Cited by 6 publications

(10 citation statements)

References 51 publications

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“…Although the use of MFCCs as features for distinguishing between individuals in other gibbon species has been limited, the many documented cases of vocal individuality across gibbon species (Haimoff and Gittins, 1985;Haimoff and Tilson, 1985;Sun et al, 2011;Wanelik et al, 2012;Feng et al, 2014) indicate that MFCCs will most likely be effective features for discriminating individuals of other gibbon species. There are numerous other options for feature extraction, including automated generation of spectro-temporal features for sound events (Sueur et al, 2008;Ross and Allen, 2014) and calculating a set of acoustic indices (Huancapaza Hilasaca et al, 2021).…”

Section: Feature Extractionmentioning

confidence: 99%

A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings

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Kier²,

Ahmad³

et al. 2023

Front. Ecol. Evol.

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Passive acoustic monitoring (PAM) allows for the study of vocal animals on temporal and spatial scales difficult to achieve using only human observers. Recent improvements in recording technology, data storage, and battery capacity have led to increased use of PAM. One of the main obstacles in implementing wide-scale PAM programs is the lack of open-source programs that efficiently process terabytes of sound recordings and do not require large amounts of training data. Here we describe a workflow for detecting, classifying, and visualizing female Northern grey gibbon calls in Sabah, Malaysia. Our approach detects sound events using band-limited energy summation and does binary classification of these events (gibbon female or not) using machine learning algorithms (support vector machine and random forest). We then applied an unsupervised approach (affinity propagation clustering) to see if we could further differentiate between true and false positives or the number of gibbon females in our dataset. We used this workflow to address three questions: (1) does this automated approach provide reliable estimates of temporal patterns of gibbon calling activity; (2) can unsupervised approaches be applied as a post-processing step to improve the performance of the system; and (3) can unsupervised approaches be used to estimate how many female individuals (or clusters) there are in our study area? We found that performance plateaued with >160 clips of training data for each of our two classes. Using optimized settings, our automated approach achieved a satisfactory performance (F1 score ~ 80%). The unsupervised approach did not effectively differentiate between true and false positives or return clusters that appear to correspond to the number of females in our study area. Our results indicate that more work needs to be done before unsupervised approaches can be reliably used to estimate the number of individual animals occupying an area from PAM data. Future work applying these methods across sites and different gibbon species and comparisons to deep learning approaches will be crucial for future gibbon conservation initiatives across Southeast Asia.

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Section: Feature Extractionmentioning

confidence: 99%

A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings

Clink

Kier²,

Ahmad³

et al. 2023

Front. Ecol. Evol.

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“…With that transition, ecologists and data scientists are now applying a multitude of data mining tools to the analysis of massive acoustic data. These include those that classify sounds (e.g., Zhao et al, 2017), sort sounds through clustering algorithms (e.g., Bellisario et al, 2019a;Bellisario et al, 2019b), reduce the massive number of acoustic features that are calculated per recording in order to reduce the multidimensionality for more efficient and less complex analysis (Dias et al, 2021;Hilasaca et al, 2021), use of acoustic recordings that are integrated with human perception data (e.g., Aletta et al, 2016) and the development and application of advanced visualization tools such as false color spectrograms (Figure 2). Software development that supports the collection, modification, analysis, fusion, and visualization of acoustic data is needed to advance acoustic remote sensing research.…”

Section: Grand Challengesmentioning

confidence: 99%

Grand Challenges in Acoustic Remote Sensing: Discoveries to Support a Better Understanding of Our Changing Planet

Pijanowski

Brown

2022

Front. Remote Sens.

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model of the seafloor derived from multibeam bathymetric measurements. Inset image shows how MBES data sets are collected. Imagery courtesy of the Dalhousie University, Seascape Ecology and Mapping Lab (www.seafloormapping.ca); (B) Synthetic Aperture Sonra data set showing a ship wreck and seafloor sediment bedforms. Data is 3 cm horizontal resolution and collected from a towed SAS system shown inset (Kraken Robotics Katfish). Imagery courtesy of Kraken Robotics (https://krakenrobotics.com/); (C) Sub-bottom profiler data showing sediment stratigraphy beneath the seafloor. The profile was collected using a towed sensor platform (Geoforce DTS). Imagery courtesy of Geoforce Group Ltd. (https://www.geoforcegroup.com/).

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“…To test the method, we built a framework for labeling of acoustic landscapes in soundcape ecology. We have employed a data set for animal group identification whose discriminant features we studied before [37] to evaluate the impact on results. Our results prove efficiency in the prediction task of labels and show reduced manual annotation effort with the methodology proposed for soundscape data.…”

Section: Background 21 Visualization In Active Learning and Labelingmentioning

confidence: 99%

“…Hereafter, we will refer to this data set as DS1. In the same region of LTER CCM, two other soundscape ecology studies were conducted: (1) [22], which aimed to assess how spatial scale (i.e., extents) influences acoustic indices responses and how these indices behave according to natural vegetation cover (%); and the authors in (2) [37] developed a method to identify the most discriminant features for categorizing sound events in soundscapes. This present study and its realization in the field of soundscape ecology represents an important tool for further ecological studies in this and other natural areas.…”

Section: Data Description and Case Studymentioning

confidence: 99%

“…For data analysis, we follow the methodology described in article [37], which first suggests a preprocessing of the audios with a set of parameters to generate the Spectrum creation. soundfile (Available in: https://pypi.org/project/SoundFile, accessed on 22 June 2021) and librosa (Available in: https://librosa.github.io/librosa, accessed on 22 June 2021) in Python were used for this.…”

Section: Data Description and Case Studymentioning

confidence: 99%

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Visual Active Learning for Labeling: A Case for Soundscape Ecology Data

2021

Self Cite

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Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models increases, it is crucial that more effective approaches to labeling are developed. In this paper, we report the development of a methodology and a framework to support labeling, with an application case as background. The methodology performs visual active learning and label propagation with 2D embeddings as layouts to achieve faster and interactive labeling of samples. The framework is realized through SoundscapeX, a tool to support labeling in soundscape ecology data. We have applied the framework to a set of audio recordings collected for a Long Term Ecological Research Project in the Cantareira-Mantiqueira Corridor (LTER CCM), localized in the transition between northeastern São Paulo state and southern Minas Gerais state in Brazil. We employed a pre-label data set of groups of animals to test the efficacy of the approach. The results showed the best accuracy at 94.58% in the prediction of labeling for birds and insects; and 91.09% for the prediction of the sound event as frogs and insects.

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Visualization and categorization of ecological acoustic events based on discriminant features

Cited by 6 publications

References 51 publications

A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings

A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings

Grand Challenges in Acoustic Remote Sensing: Discoveries to Support a Better Understanding of Our Changing Planet

Visual Active Learning for Labeling: A Case for Soundscape Ecology Data

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