• Corpus ID: 460993

Audio Based Bird Species Identification using Deep Learning Techniques

  title={Audio Based Bird Species Identification using Deep Learning Techniques},
  author={Elias Sprengel and Martin Jaggi and Yannic Kilcher and Thomas Hofmann},
  booktitle={Conference and Labs of the Evaluation Forum},
Reference EPFL-CONF-229232 URL: http://ceur-ws.org/Vol-1609/16090547.pdf Record created on 2017-06-21, modified on 2017-07-11 

Figures and Tables from this paper

Audio-based Bird Species Identification with Deep Convolutional Neural Networks

The proposed approach is evaluated in the BirdCLEF 2018 campaign and provides the best system in all subtasks and surpasses previous state-of-the-art by 15.8 % identifying foreground species and 20.2 % considering also background species.

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

This work discusses the attempt of large-scale bird species identification using the 2018 BirdCLEF baseline system and its implications for future research in acoustic event classification.

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.

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.

A Multi-modal Deep Neural Network approach to Bird-song Identication

A multi-modal Deep Neural Network (DNN) approach for bird song identification takes both audio samples and metadata as input and achieves 1.

S2I-Bird: Sound-to-Image Generation of Bird Species using Generative Adversarial Networks

This paper proposes a novel deep learning model that generates bird images from their corresponding sound information using conditional generative adversarial networks (cGANs) with auxiliary classifiers and demonstrates that this model produces better image generation results.

N ov 2 01 8 A Multi-modal Deep Neural Network approach to Birdsong identification

A multi-modal Deep Neural Network (DNN) approach for bird song identification takes both audio samples and metadata as input and achieves 1.

Recognizing Bird Species in Audio Files Using Transfer Learning

It is shown that fine-tuning a pre-trained convolutional neural network performs better than training a neural network from scratch in the context of the BirdCLEF 2017 task.

Acoustic bird detection with deep convolutional neural networks

This paper presents deep learning techniques for acoustic bird detection and provides the best system for the task and surpasses previous state-of-the-art achieving an area under the curve (AUC) above 95 % on the public challenge leaderboard.

Bird Sound Classification Using Convolutional Neural Networks

The inception model achieved 0.16 classification mean average precision (c-mAP) and ranked the second place among five teams that successfully submitted their predictions in the BirdCLEF2019 competition.



LifeCLEF Bird Identification Task 2017

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.

Bird Song Classification in Field Recordings: Winning Solution for NIPS4B 2013 Competition *

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.

Shared Nearest Neighbors Match Kernel for Bird Songs Identification - LifeCLEF 2015 Challenge

This paper presents a new fine-grained audio classification technique designed and experimented in the context of the LifeCLEF 2015 bird species identification challenge, and introduces a new match kernel based on the shared nearest neighbors of the low level audio features extracted at the frame level.

Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection

This work introduces a convolutional neural network (CNN) with a large input field for AED that significantly outperforms state of the art methods including Bag of Audio Words (BoAW) and classical CNNs, achieving a 16% absolute improvement.

Exploring Data Augmentation for Improved Singing Voice Detection with Neural Networks

A range of label-preserving audio transformations are applied and pitch shifting is found to be the most helpful augmentation method for music data augmentation, reaching the state of the art on two public datasets.

LifeCLEF 2016: Multimedia Life Species Identification Challenges

The LifeCLEF lab proposes to evaluate 3 challenges related to multimedia information retrieval and fine-grained classification problems in 3 domains based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios.

Improved Automatic Bird Identification through Decision Tree based Feature Selection and Bagging

This paper presents a machine learning technique for bird species identification at large scale. It automatically identifies about a thousand different species in a large number of audio recordings

Training Very Deep Networks

A new architecture designed to overcome the challenges of training very deep networks, inspired by Long Short-Term Memory recurrent networks, which allows unimpeded information flow across many layers on information highways.

Song "Dialects" in Three Populations of White-Crowned Sparrows

This paper seeks to provide some of the necessary information about song variation in the individual and in a population, both at one time and from year to year, and also by comparing songs in three populations, two adjacent and one distant.

The Species of Birds of South America and Their Distribution Rodolphe Meyer de Schauensee