LifeCLEF Bird Identification Task 2014 (original) (raw)

LifeCLEF Bird Identification Task 2017

2017

The LifeCLEF challenge BirdCLEF offers a large-scale proving ground for system-oriented evaluation of bird species identification based on audio recordings of their sounds. One of its strengths is that it uses data collected through Xeno-canto, the worldwide community of bird sound recordists. This ensures that BirdCLEF is close to the conditions of real-world application, in particular with regard to the number of species in the training set (1500). The main novelty of the 2017 edition of BirdCLEF was the inclusion of soundscape recordings containing time-coded bird species annotations in addition to the usual Xeno-canto recordings that focus on a single foreground species. This paper reports an overview of the systems developed by the five participating research groups, the methodology of the evaluation of their performance, and an analysis and discussion of the results obtained.

Overview of BirdCLEF 2019: Large-Scale Bird Recognition in Soundscapes

2019

The BirdCLEF challenge-as part of the 2019 LifeCLEF Lab [7]-offers a large-scale proving ground for system-oriented evaluation of bird species identification based on audio recordings. The challenge uses data collected through Xeno-canto, the worldwide community of bird sound recordists. This ensures that BirdCLEF is close to the conditions of real-world application, in particular with regard to the number of species in the training set (659). In 2019, the challenge was focused on the difficult task of recognizing all birds vocalizing in omni-directional soundscape recordings. Therefore, the dataset of the previous year was extended with more than 350 hours of manually annotated soundscapes that were recorded using 30 field recorders in Ithaca (NY, USA). This paper describes the methodology of the conducted evaluation as well as the synthesis of the main results and lessons learned.

Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System

arXiv (Cornell University), 2018

Reliable identification of bird species in recorded audio files would be a transformative tool for researchers, conservation biologists, and birders. In recent years, artificial neural networks have greatly improved the detection quality of machine learning systems for bird species recognition. We present a baseline system using convolutional neural networks. We publish our code base as reference for participants in the 2018 LifeCLEF bird identification task and discuss our experiments and potential improvements.

LifeCLEF Bird Identification Task 2016: The arrival of Deep learning

Conference and Labs of the Evaluation Forum, 2016

The LifeCLEF bird identification challenge provides a largescale testbed for the system-oriented evaluation of bird species identification based on audio recordings. One of its main strength is that the data used for the evaluation is collected through Xeno-Canto, the largest network of bird sound recordists in the world. This makes the task closer to the conditions of a real-world application than previous, similar initiatives. The main novelty of the 2016-th edition of the challenge was the inclusion of soundscape recordings in addition to the usual xeno-canto recordings that focus on a single foreground species. This paper reports the methodology of the conducted evaluation, the overview of the systems experimented by the 6 participating research groups and a synthetic analysis of the obtained results.

Overview of BirdCLEF 2018: Monospecies vs. Sundscape Bird Identification

2018

The BirdCLEF challenge offers a large-scale proving ground for system-oriented evaluation of bird species identification based on au- dio recordings of their sounds. One of its strengths is that it uses data collected through Xeno-canto, the worldwide community of bird sound recordists. This ensures that BirdCLEF is close to the conditions of real- world application, in particular with regard to the number of species in the training set (1500). Two main scenarios are evaluated: (i) the identifi- cation of a particular bird species in a recording, and (ii), the recognition of all species vocalising in a long sequence (up to one hour) of raw sound- scapes that can contain tens of birds singing more or less simultaneously. This paper reports an overview of the systems developed by the six participating research groups, the methodology of the evaluation of their performance, and an analysis and discussion of the results obtained.

A Baseline for Large-Scale Bird Species Identification in Field Recordings

CLEF (Working Notes), 2018

The LifeCLEF bird identifcation task poses a difficult challenge in the domain of acoustic event classification. Deep learning techniques have greatly impacted the field of bird sound recognition in recent years. We discuss our attempt of large-scale bird species identification using the 2018 BirdCLEF baseline system.

Automatic Identification of Bird Species from Audio

Lecture Notes in Computer Science, 2021

Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which makes their real time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology able to identify bird species by its chirp. In this paper we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks and Recurrent Neural Networks to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients. We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.

Overview of BirdCLEF 2020: Bird Sound Recognition in Complex Acoustic Environments

2020

Passive acoustic monitoring is a cornerstone of the assessment of ecosystem health and the improvement of automated assessment systems has the potential to have a transformative impact on global biodiversity monitoring, at a scale and level of detail that is impossible with manual annotation or other more traditional methods. The BirdCLEF challenge—as part of the 2020 LifeCLEF Lab [12]—focuses on the development of reliable detection systems for avian vocalizations in continuous soundscape data. The goal of the task is to localize and identify all audible birds within the provided soundscape test set. This paper describes the methodology of the conducted evaluation as well as the synthesis of the main results and lessons learned.

Automated bird sound recognition in realistic settings

2018

We evaluated the effectiveness of an automated bird sound identification system in a situation that emulates a realistic, typical application. We trained classification algorithms on a crowd-sourced collection of bird audio recording data and restricted our training methods to be completely free of manual intervention. The approach is hence directly applicable to the analysis of multiple species collections, with labelling provided by crowd-sourced collection. We evaluated the performance of the bird sound recognition system on a realistic number of candidate classes, based upon typical numbers that would be encountered in real conditions.  Methods: We used a threshold selection method to separate clean bird sound from silence in the crowd-sourced recordings of the training dataset. Test data were obtained from hand-curated recordings and chosen to correspond to an application scenario where the end user selects an excerpt of clean bird sound and presents it, with no extra information, to the identification system. We investigated the use of two canonical classification methods, chosen due to their widespread use and ease of interpretation, namely a k Nearest Neighbour (kNN) classifier with histogram-based features and a Support Vector Machine (SVM) with timesummarisation features. We further investigated the use of a certainty measure, derived from the output probabilities of the classifiers, to enhance the interpretability and reliability of the class decisions.  Results: Our results demonstrate that both identification methods achieved similar performance, but we argue that the use of the k Nearest Neighbour classifier offers somewhat more flexibility. Furthermore, we show that employing an 3 outcome certainty measure provides a valuable and consistent indicator of the reliability of classification results.  Wider implications: Our use of generic training data and our investigation of probabilistic classification methodologies that can flexibly address the variable number of candidate species/classes that are expected to be encountered in the field, directly contribute to the development of a practical bird sound identification system with potentially global application. Further, we show that certainty measures associated with identification outcomes can significantly contribute to the practical usability of the overall system.

Visual and Acoustic Identification of Bird Species

This paper combines both approaches for bird species identification by extracting visual features from bird images and acoustic features from bird calls. Some bird species are rarely found in certain regions, and it's difficult to track them if done the prediction is difficult. In order to withstand this issue, we've come across a significant and easier way to recognize these bird species based on their features. We've used BirdCLEF 2022 dataset for the audio segment and the BIRDS 400 dataset for the image segment for the training and testing parts. Since among most of the approaches, we have studied CNN as vanquishing, therefore we've used CNN for both visual as well as acoustic identification. CNN is the strong assemblage of ML which has proven efficient in image processing. Our project has become attractive because of the techniques and recent advances within the domain of deep learning. With novel preprocessing and data augmentation methods, we train a convolutional neural network on the largest public obtainable dataset. By establishing a dataset and using the rule of similarity comparison algorithms, our system can provide the best results. By using our system, everyone will simply be able to determine the species of the particular bird which they provide image/audio or both as input.