• Corpus ID: 51941432

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

@inproceedings{Kahl2018ABF,
  title={A Baseline for Large-Scale Bird Species Identification in Field Recordings},
  author={Stefan Kahl and Thomas Wilhelm-Stein and Holger Klinck and Danny Kowerko and Maximilian Eibl},
  booktitle={Conference and Labs of the Evaluation Forum},
  year={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. 
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7 References

Large-Scale Bird Sound Classification using Convolutional Neural Networks

A method for large-scale bird sound classification in the context of the LifeCLEF 2017 bird identification task was summarized, using a variety of convolutional neural networks to generate features extracted from visual representations of field recordings.

Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System

A baseline system using convolutional neural networks for bird species recognition is presented and the code base is published as reference for participants in the 2018 LifeCLEF bird identification task.

Audio Based Bird Species Identification using Deep Learning Techniques

Reference EPFL-CONF-229232 URL: http://ceur-ws.org/Vol-1609/16090547.pdf Record created on 2017-06-21, modified on 2017-07-11

Two convolutional neural networks for bird detection in audio signals

Two approaches to detect the presence of bird calls in audio recordings using convolutional neural networks on mel spectrograms are presented and it is found that despite very different architectures, both approaches can be tuned to perform equally well.

Overview of LifeCLEF 2018: A Large-Scale Evaluation of Species Identification and Recommendation Algorithms in the Era of AI

The methodology of the conducted evaluations as well as the synthesis of the main results and lessons learned are described to push the boundaries of the state-of-the-art in several research directions.

Distilling the Knowledge in a Neural Network

This work shows that it can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model and introduces a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse.

Overview of BirdCLEF 2018: monophone vs

  • soundscape bird identification. In: CLEF working notes 2018
  • 2018