Search Results for author: Holger Klinck

Found 8 papers, 5 papers with code

Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System

3 code implementations19 Apr 2018 Stefan Kahl, Thomas Wilhelm-Stein, Holger Klinck, Danny Kowerko, Maximilian Eibl

Reliable identification of bird species in recorded audio files would be a transformative tool for researchers, conservation biologists, and birders.

BIG-bench Machine Learning Bird Audio Detection

GIBBONFINDR: An R package for the detection and classification of acoustic signals

1 code implementation6 Jun 2019 Dena J. Clink, Holger Klinck

The recent improvements in recording technology, data storage and battery life have led to an increased interest in the use of passive acoustic monitoring for a variety of research questions.

Acoustic Modelling BIG-bench Machine Learning +1

Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization

no code implementations1 Nov 2019 Vincent Lostanlen, Kaitlin Palmer, Elly Knight, Christopher Clark, Holger Klinck, Andrew Farnsworth, Tina Wong, Jason Cramer, Juan Pablo Bello

This paper proposes to perform unsupervised detection of bioacoustic events by pooling the magnitudes of spectrogram frames after per-channel energy normalization (PCEN).

Noise Estimation speech-recognition +1

Parsing Birdsong with Deep Audio Embeddings

no code implementations20 Aug 2021 Irina Tolkova, Brian Chu, Marcel Hedman, Stefan Kahl, Holger Klinck

Monitoring of bird populations has played a vital role in conservation efforts and in understanding biodiversity loss.

Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms

1 code implementation5 Apr 2023 Pu Li, Marie Roch, Holger Klinck, Erica Fleishman, Douglas Gillespie, Eva-Marie Nosal, Yu Shiu, Xiaobai Liu

To overcome this limitation, we present a framework of stage-wise generative adversarial networks (GANs), which compile new whistle data suitable for deep model training via three stages: generation of background noise in the spectrogram, generation of whistle contours, and generation of whistle signals.

Data Augmentation

Global birdsong embeddings enable superior transfer learning for bioacoustic classification

1 code implementation12 Jul 2023 Burooj Ghani, Tom Denton, Stefan Kahl, Holger Klinck

With the advent of deep learning models, classification of important signals from these datasets has markedly improved.

Audio Classification Decision Making +1

BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics

1 code implementation12 Dec 2023 Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer, Stefan Kahl, Holger Klinck, Tom Denton, Vincent Dumoulin

The ability for a machine learning model to cope with differences in training and deployment conditions--e. g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases.

Information Retrieval Representation Learning +1

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