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7 results for “sound annotations”
To bee or not to bee: An annotated dataset for beehive sound recognition
<p><strong>-- Dataset documentation --</strong></p> <p><br> <strong>1- Introduction</strong></p> <p>The present dataset was developed in the context of our work in [1] that focus on the automatic recognition of beehive sounds. The problem is posed as the classification of sound segments in two classes: Bee and noBee. The novelty of the explored approach and the need for annotated data, dictated the construction of such dataset.</p> <p><strong>2- Description</strong></p> <p><strong>2.1- Audio recordings:</strong></p> <p>The annotated dataset was developed based on a selected set of recordings acquired in the context of two different projects: the Open Source Beehive (OSBH) project [2] and the NU-Hive project [3]. Both projects main goal is to develop a beehive monitoring system capable of identifying and predict certain events and states of the hive that are of interest to the beekeeper. Among many different variables that can be measured and that help the recognition of different states of the hive, the analysis and use of the sound the bees produce is a big focus for both projects.</p> <p>The recordings from the OSBH project were acquired through a citizen science initiative which asked people from the general public to record the sound from their beehives together with the registering of the hive state at the moment. Because of the amateur and collaborative nature of this project, the recordings from the OSBH project present great diversity due to the very different conditions in which the signals were acquired: different recording devices used, different environments where the hives were placed, and even different position for the microphones inside the hive. This variety of settings makes this dataset a very interesting tool to help evaluate and challenge the methods developed.</p> <p>The NU-Hive project is a comprehensive effort of data acquisition, concerning not only sound, but a vast amount of variables that will allow the study of bees behaviors and other unknown aspects. The selected recordings are taken from 2 hives and labeled regarding two states: queen bee is present, and queen bee not present. Contrary to the OSBH project recordings, the recordings from the NU-Hive project are from a much more controlled and homogeneous environment. Here the occurring external sounds are mainly traffic, car honks and birds.</p> <p><strong>The annotated dataset:</strong></p> <p>For each selected recording, time segments are labeled as Bee or noBee depending on the perceived source of the sound signal being from bees or external to the hive.</p> <p>The whole annotated dataset consists of 78 recordings of varying lengths which make up for a total duration of approximately 12 hours of which 25% is annotated as noBee events.</p> <p>About 60% of the recordings are from the NU-Hive dataset and represent 2 hives, the remaining are recordings from the OSBH dataset and 6 different hives. The recorded hives are from 3 main locations: North America, Australia and Europe.</p> <p> </p> <p><strong>2- Annotation procedure<a href="http://localhost:8888/notebooks/Dropbox/QMUL/BEESzzzz/Data/Annotations/readme.ipynb#2--Annotation-procedure">¶</a></strong></p> <p>The annotation procedure consists in hearing the selected recordings and marking the beginning and the end of every sound that could not be recognized as a beehive sound. The recognition of external sounds is based primarily on the perceived heard sounds, but a visual aid is also used by visualizing the log-mel frequency spectrum of the signal. All the above are functionalities offered by the Sonic Visualiser software, which was used by two volunteers that are neither bee-specialists nor specially trained in sound annotation tasks.</p> <p>By marking these pairs of moments corresponding to the beginning and end of external sound periods, we are able to get the whole recording labeled into Bee and noBee intervals. Thus in the resulting Bee intervals only pure beehive sounds, (no external sounds) should be perceived for the entirety of the segment. The noBee intervals refer to periods where an external sound can be perceived (superimposed to the bee sounds).</p> <p> </p> <p><strong>File Structure:</strong></p> <p>Each audio file is coupled with its corresponding annotation file, identified by the same name and extension <em>.lab</em>.<br> For convenience, all the annotations are collected in a single master label file named <em>beeAnnotations.mlf</em></p> <p>The <em>.lab</em> files consist of : </p> <ul> <li>First row identifies the audio file to which the annotations refer to.</li> <li>Each line after that describes an interval with starting time point, end time point and label. The time points are expressed in seconds.</li> </ul> <p>Below is an example of such an annotation file: </p> <pre><code>Hive3_20_07_2017_QueenBee_H3_audio_15_30_00 0 78.45 bee 78.46 78.95 nobee 78.96 103.92 bee 103.93 112.48 nobee 112.49 152.48 bee . </code></pre> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International License.<br> When using this dataset, please cite [1]:</p> <p>[1] I. Nolasco and E. Benetos, “To bee or not to bee: Investigating machine learning approaches to beehive sound recognition,” in Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2018, submitted.</p> <p>[2] “Open Source Beehives Project,” https://www.osbeehives.com/.</p> <p>[3] S. Cecchi, A. Terenzi, S. Orcioni, P. Riolo, S. Ruschioni, and N. Isidoro, “A preliminary study of sounds emitted by honey bees in a beehive,” in Audio Engineering Society Convention 144, 2018.</p>
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
<p>Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities. </p>
Manual acoustic signal annotation for species from sound libraries Jacques Vielliard Neotropical Sound Library
<h3>Description</h3> <p>We present a multispecies dataset of active acoustic recordings of anuran amphibians deposited in the Jacques Vielliard Neotropical Sound Library from the original AnuraSet localities. Some audio files were donated by professional acousticians, due to the small sample size of some species. The dataset consists of 270 audio files with a total of 305,211 minutes of annotated acoustic activity from 12 prioritised species. </p> <p>The annotation quality was classified as ‘Clear’ by 46%, ‘Medium’ by 43% and ‘Far’ by 7%. The species with the highest recorded acoustic activity were Boana faber, Boana albomarginata, and Physalaemus cuvieri represented by 51, 50, and 50 recordings respectively, while the species with the lowest acoustic activity was Dendropsophus nahdereri represented by four recordings. </p> <p>To access the file you can request it at the following link: </p> <p>https://www2.ib.unicamp.br/fnjv/ </p> <h3>Reference</h3> <p>Paper: https://www.nature.com/articles/s41597-023-02666-2</p> <p>Repository: https://github.com/soundclim/anuraset</p> <p>Webpage: https://soundclim.github.io/anuraweb/<br><br></p> <h3>Related projects</h3> <p>https://research.google/blog/in-search-of-a-generalizable-method-for-source-free-domain-adaptation/</p> <h3>Citation</h3> <p>Arcila-Pérez LF, Ulloa JS & Soundclim Network (2024). Manual acoustic signal annotation for species from sound libraries Jacques Vielliard Neotropical Sound Library. Zenodo. https://doi.org/10.5281/zenodo.13786714</p>
Data from: AviaNZ: a future-proofed program for annotation and recognition of animal sounds in long-time field recordings
The routine collection of long‐time acoustic recordings of animals in the field presents new challenges in data analysis. While many terabytes of data are collected annually, effective use of this noisy, highly variable data require skilled humans to manually identify calls. While computer programs to automatically analyse these recordings are becoming available, it is important that they are user‐friendly and easy‐to‐use, so that everybody – citizen scientists, wildlife managers, researchers – can take advantage of them, and that they keep the human in the loop so analyses carried out this year are comparable both to manual call counts from the past, and more accurate automated analyses performed in the future. We present the AviaNZ program, which is designed to achieve these goals: the software includes methods for simple, rapid manual annotation of recordings, denoising and segmentation methods, and a training procedure by which the user can prepare their own filters to automatically recognize individual species. The software can run in batch mode, automatically processing folders of field recordings, and then present the outputs to enable the quick and easy review of the results. Finally, the outputs are presented in a variety of spreadsheets to enable different statistical analyses to be performed. We describe the various workflows of manually and semi‐automatically processing sound files, annotating them to train automatic filters, using those filters in batch mode, and how the software facilitates rapid evaluation of the automated analysis. A demonstration of the software, comparing manual and automatic detection of calls of the little spotted kiwi Apteryx owenii is given. It shows that while the automatic detection does produce false positives, human correction of these is far faster than manual review of the whole sound file. AviaNZ is a freely available open‐source standalone program. Our experience shows that it can be used by anybody quickly and easily. However, for experienced users it is easily customizable and extendable. By enabling everybody involved with acoustic bird recording to quickly and easily analyse their own data, while future‐proofing it by keeping the human in the loop, we are enabling acoustic field recordings to meet their potential.
Data from: AviaNZ: a future-proofed program for annotation and recognition of animal sounds in long-time field recordings
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Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
Open the record for dataset details and reuse information.
BCN Dataset: an Annotated Night Urban Sounds dataset
<p>This dataset contains 363 minutes and 53 seconds of real-world audio recordings made at the city center of Barcelona. The dataset has been carefully labelled and is organized in three different audio files (.wav files) with their corresponding labels files. </p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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