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

With additional methods like filtering audio chunks with random transfer function or applying local time stretching and pitch shifting in time domain, identification

Furthermore predictions of the two best snapshots per model (regarding performance on the validation set) were chosen for averaging instead of just one, resulting in

Section 2 details the layout and training of a Convolutional Neural Network (CNN) processing the audio signals, Section 3 describes a Multilayer Perceptron (MLP) predicting the

Then, the generated picture files that were transformed from the reduced training set were used to fine-tune a pre-trained Inception-v3 convolutional neu- ral network.. Pre-training

Keywords: Bioacoustics, Large-Scale Classification, Convolutional Neural Networks, Audio Features, Bird Sound Identification, BirdCLEF 2017.. Source code of this

The LifeCLEF bird identification challenge provides a large- scale testbed for the system-oriented evaluation of bird species identifi- cation based on audio recordings.. One of

The approach is evaluated in the context of the LifeCLEF 2016 bird identification task - an open challenge conducted on a dataset containing 34 128 audio recordings representing

Our network archi- tecture achieves a mean average precision score of 0.686 when predicting the main species of each sound file and scores 0.555 when background species are used