Corpus ID: 24946551

Large-Scale Bird Sound Classification using Convolutional Neural Networks

@inproceedings{Kahl2017LargeScaleBS,
  title={Large-Scale Bird Sound Classification using Convolutional Neural Networks},
  author={Stefan Kahl and Thomas Wilhelm-Stein and Hussein Hussein and Holger Klinck and Danny Kowerko and Marc Ritter and Maximilian Eibl},
  booktitle={CLEF},
  year={2017}
}
Identifying bird species in audio recordings is a challenging field of research. In this paper, we summarize a method for large-scale bird sound classification in the context of the LifeCLEF 2017 bird identification task. We used a variety of convolutional neural networks to generate features extracted from visual representations of field recordings. The BirdCLEF 2017 training dataset consist of 36.496 audio recordings containing 1500 different bird species. Our approach achieved a mean average… Expand
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References

SHOWING 1-10 OF 21 REFERENCES
Recognizing Bird Species in Audio Recordings using Deep Convolutional Neural Networks
TLDR
This paper summarizes a method for purely audio-based bird species recognition through the application of convolutional neural networks, evaluated in the context of the LifeCLEF 2016 bird identification task an open challenge conducted on a dataset representing 999 bird species from South America. Expand
LifeCLEF Bird Identification Task 2017
TLDR
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 are reported. Expand
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
Bird Song Classification in Field Recordings: Winning Solution for NIPS4B 2013 Competition *
TLDR
The goal of the NIPS4B competition is to identify which of the 87 sound classes of birds and amphibians are present in 1000 continuous wildlife recordings, using only the provided audio files and machine learning algorithms for automatic pattern recognition. Expand
AENet: Learning Deep Audio Features for Video Analysis
TLDR
A convolutional neural network operating on a large temporal input allows for an audio event detection system end to end and performs transfer learning and shows that the model learned generic audio features, similar to the way CNNs learn generic features on vision tasks. Expand
Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual RecognitionExpand
Learning Spatiotemporal Features with 3D Convolutional Networks
TLDR
The learned features, namely C3D (Convolutional 3D), with a simple linear classifier outperform state-of-the-art methods on 4 different benchmarks and are comparable with current best methods on the other 2 benchmarks. Expand
ImageNet classification with deep convolutional neural networks
TLDR
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective. Expand
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
TLDR
This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities. Expand
Deep Residual Learning for Image Recognition
TLDR
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. Expand
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