README.md

---
license: mit
task_categories:
- audio-classification
language:
- en
- es
tags:
- biology
- synthetic
pretty_name: BIRDeep_AudioAnnotations
size_categories:
- n<1K
authors:
- family-names: Márquez-Rodríguez
  given-names: Alba
  affiliation: Estación Biológica de Doñana, Dept. of Ecology and Evolution & Universidad de Córdoba, Dept. of Informatics and Numeric Analysis
  city: Seville, Córdoba
  country: Spain
  
- family-names: Muñoz-Mohedano
  given-names: Miguel Ángel
  affiliation: Estación Biológica de Doñana, Dept. of Ecology and Evolution
  city: Seville
  country: Spain
  
- family-names: Marín Jiménez
  given-names: Manuel Jesús
  affiliation: Universidad de Córdoba, Dept. of Informatics and Numeric Analysis
  city: Córdoba
  country: Spain
  
- family-names: Santamaría García
  given-names: Eduardo
  affiliation: Estación Biológica de Doñana, Dept. of Ecology and Evolution
  city: Seville
  country: Spain
  
- family-names: Bastianelli
  given-names: Giulia
  affiliation: Estación Biológica de Doñana, ICTS-Doñana (Infraestructura Científico-Técnica Singular de Doñana)
  city: Seville
  country: Spain
  
- family-names: Mendoza
  given-names: Irene
  affiliation: Estación Biológica de Doñana, Dept. of Ecology and Evolution 
  city: Seville
  country: Spain
  
---

# BIRDeep Audio Annotations

<!-- Provide a quick summary of the dataset. -->

The BIRDeep Audio Annotations dataset is a collection of bird vocalizations from Doñana National Park, Spain. It was created as part of the BIRDeep project, which aims to optimize the detection and classification of bird species in audio recordings using deep learning techniques. The dataset is intended for use in training and evaluating models for bird vocalization detection and identification.

The research code and further information is available at the [Github Repository](https://github.com/GrunCrow/BIRDeep_BirdSongDetector_NeuralNetworks).

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Curated by:** Estación Biológica de Doñana (CSIC) and Universidad de Córdoba
- **Funded by:** BIRDeep project (TED2021-129871A-I00), which is funded by MICIU/AEI/10.13039/501100011033 and the 'European Union NextGenerationEU/PRTR', as well as grants PID2020-115129RJ-I00 from MCIN/AEI/10.13039/501100011033.
- **Shared by:** BIRDeep Project
- **Language(s):** English
- **License:** MIT

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Code Repository:** [BIRDeep Neural Networks](https://github.com/GrunCrow/BIRDeep_NeuralNetworks)
- **Paper:** Decoding the Sounds of Doñana: Advancements in Bird Detection and Identification Through Deep Learning

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

The dataset is intended for use in training and evaluating models for bird vocalization detection and identification. It can be used to automate the annotation of these recordings, facilitating relevant ecological studies.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset includes audio data categorized into 38 different classes, representing a variety of bird species found in the park. The data was collected from three main habitats across nine different locations within Doñana National Park, providing a diverse range of bird vocalizations.

The distribution of the 38 different classes through the 3 subdatasets (train, validation and test) is the following:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/669e33c571c6596608284b24/ZdKKSVajKrBCtWHVQs4bm.png)

## Data Files Description

There are 3 `.CSV` files that contain all the metadata related to each split of the dataset (train, validation, and test). Each of these `.CSV` files includes the following information. Each row represents one annotation (an annotated bird song). There might be more than one row per audio.

- **path**: Relative path from the `Audio` folder to the corresponding audio. For images, change the file format to `.PNG` and use the `images` folder instead of the `Audios` folder.
- **annotator**: Expert ornithologist who annotated the detection.
- **recorder**: Code of the recorder; see below for the mapping of recorder, location, and coordinates.
- **date**: Date of the recording.
- **time**: Time of the recording.
- **audio_duration**: Duration of the audio (all are 1-minute audios).
- **start_time**: Start time of the annotated bird song relative to the full duration of the audio.
- **end_time**: End time of the annotated bird song relative to the full duration of the audio.
- **low_frequency**: Lower frequency of the annotated bird song.
- **high_frequency**: Higher frequency of the annotated bird song.
- **specie**: Species to which the annotation belongs.
- **bbox**: Bounding box coordinates in the image (YOLOv8 format).

Each annotation has been adapted to the YOLOv8 required format, which follows the same folder structure as the image folder (which is the same as the `Audio` folder) for a labels folder. It contains a `.TXT` file for each image with one row per annotation, including the species and bounding box.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

The dataset was created to improve the accuracy and efficiency of bird species identification using deep learning models for our study case (Doñana National Park). It addresses the challenge of managing large datasets of acoustic recordings for identifying species of interest in ecoacoustics studies.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

Audio recordings were collected from three main habitats across nine different locations within Doñana National Park using automatic audio recorders (AudioMoths). See map below. 

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/669e33c571c6596608284b24/ENWc533IEGsKtkaDHx3Wk.jpeg)

The names of the places correspond to the following recorders and coordinates:

| Number | Habitat    | Place Name        | Recorder | Lat        | Lon          | Installation Date |
|--------|------------|-------------------|----------|------------|--------------|-------------------|
| Site 1 | low shrubland | Monteblanco       | AM1      | 37.074     | -6.624       | 03/02/2023        |
| Site 2 | high shrubland | Sabinar           | AM2      | 37.1869444 | -6.720555556 | 03/02/2023        |
| Site 3 | high shrubland | Ojillo            | AM3      | 37.2008333 | -6.613888889 | 03/02/2023        |
| Site 4 | low shrubland | Pozo Sta Olalla   | AM4      | 37.2202778 | -6.729444444 | 03/02/2023        |
| Site 5 | ecotone    | Torre Palacio     | AM8      | 37.1052778 | -6.5875      | 03/02/2023        |
| Site 6 | ecotone    | Pajarera          | AM10     | 37.1055556 | -6.586944444 | 03/02/2023        |
| Site 7 | ecotone    | Caño Martinazo    | AM11     | 37.2086111 | -6.512222222 | 03/02/2023        |
| Site 8 | marshland  | Cancela Millán    | AM15     | 37.0563889 | -6.6025      | 03/02/2023        |
| Site 9 | marshland  | Juncabalejo       | AM16     | 36.9361111 | -6.378333333 | 03/02/2023        |

All recording times and datasets are in UTC format.
			

#### Data producers

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

The data was produced by researchers from Estación Biológica de Doñana and Universidad de Córdoba. A research center and University at the south zone of Spain, close to the study region, National Park of Doñana.

### Annotations

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
Approximately 500 minutes of audio data were annotated, prioritizing times when birds are most active to capture as many songs as possible, specifically from a few hours before dawn until midday.

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

Annotations were made manually by experts, resulting in 3749 annotations representing 38 different classes. In addition to the species-specific classes, other general classes were distinguished: Genus (when the species was unknown but the genus of the species was distinguished), a general "Bird" class, and a "No Audio" class for recordings that contain only soundscape without bird songs. 

As the Bird Song Detector only has two classes, labels were reclassified as "Bird" or "No bird" for recordings that include only soundscape background without biotic sound or whether biotic sounds were non-avian.

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->
- Eduardo Santamaría García, Estación Biológica de Doñana, Dept. of Ecology and Evolution, Sevilla, Spain
- Giulia Bastianelli, Estación Biológica de Doñana, ICTS-Doñana (Infraestructura Científico-Técnica Singular de Doñana), Sevilla, Spain

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The dataset may have biases due to the specific ecological context of Doñana National Park and the focus on bird vocalizations. It also exhibits class imbalance, with varying frequencies of annotations across different bird species classes. Additionally, the dataset contains inherent challenges related to environmental noise.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be aware of the ecological context and potential biases when using the dataset. They should also consider the class imbalance and the challenges related to environmental noise.

## More Information

This dataset incorporates synthetic background audio, which has been created by introducing noise and modifying the original audio intensities. This process, known as Data Augmentation, enhances the robustness of the dataset. Additionally, a subset of the ESC-50 dataset, which is a widely recognized benchmark for environmental sound classification, has also been included to enrich the diversity of the dataset. These additional datasets can be excluded as they are in separate folders within the root folders for audios, images, and labels (`Data Augmentation` and `ESC50`). Annotations for these datasets should be removed from the CSV files if they are not used in processing the dataset.

The synthetic audio was created using a Python script that took the original background audio recordings and modified their intensities and shifted them. This method allowed for the introduction of noise and variations in the audio, simulating different recording conditions and enhancing the dataset's robustness.

## Dataset Card Authors and Affiliations

- Alba Márquez-Rodríguez, Estación Biológica de Doñana, Dept. of Ecology and Evolution & Universidad de Córdoba, Dept. of Informatics and Numeric Analysis
- Miguel Ángel Muñoz-Mohedano, Estación Biológica de Doñana, Dept. of Ecology and Evolution
- Manuel Jesús Marín-Jiménez, Universidad de Córdoba, Dept. of Informatics and Numeric Analysis
- Eduardo Santamaría-García, Estación Biológica de Doñana, Dept. of Ecology and Evolution
- Giulia Bastianelli, Estación Biológica de Doñana, ICTS-Doñana (Infraestructura Científico-Técnica Singular de Doñana)
- Irene Mendoza, Estación Biológica de Doñana, Dept. of Ecology and Evolution

## Citation

```
@misc{birdeep_audioannotations_2024,
    author = {M{\'a}rquez-Rodr{\'i}guez, Alba and Muñoz-Mohedano, Miguel {\'A}ngel and Mar{\'i}n-Jim{\'e}nez, Manuel Jes{\'u}s and Santamar{\'i}a-Garc{\'i}a, Eduardo and Bastianelli, Giulia and Mendoza, Irene},
    title = {BIRDeepAudioAnnotations (Revision 4cf0456)},
    url = {https://huggingface.co/datasets/GrunCrow/BIRDeepAudioAnnotations},
    year = {2024},
    doi = {10.57967/hf/2801},
    publisher = {Hugging Face}
}
```

## Dataset Card Contact

Alba Márquez-Rodríguez - [email protected]