Recognizing bird species in diverse soundscapes under weak supervision
We present a robust classification approach for avian vocalization in complex and diverse soundscapes, achieving second place in the BirdCLEF2021 challenge. We illustrate how to make full use of pre-trained convolutional neural networks, by using an efficient modeling and training routine supplemented by novel augmentation methods. Thereby, we improve the generalization of weakly labeled crowd-sourced data to productive data collected by autonomous recording units. As such, we illustrate how to progress towards an accurate automated assessment of avian population which would enable global biodiversity monitoring at scale, impossible by manual annotation.
Code (1)
Tasks
Robust classificationSimilar Papers 제목 키워드 기반
Dynamic Multi-Species Bird Soundscape Generation with Acoustic Patterning and 3D Spatialization
Generation of dynamic, scalable multi-species bird soundscapes remains a significant challenge in computer music and algorithmic sound design. Birdsongs involve rapid frequency-modulated chirps, complex amplitude envelop…
A strongly annotated passive acoustic dataset for tropical bird monitoring
Passive acoustic monitoring enables continuous, non-invasive biodiversity assessment across diverse ecosystems. The scale of these datasets has driven the adoption of machine learning, with supervised approaches showing …
Time-frequency localization of bird calls in dense soundscapes
Passive acoustic monitoring enables large-scale observation of wildlife, but most bioacoustic classifiers only predict species presence in a time window without localizing vocalizations precisely in time or frequency, li…
Object DetectionTransfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification
We present working notes on transfer learning with semi-supervised dataset annotation for the BirdCLEF 2023 competition, focused on identifying African bird species in recorded soundscapes. Our approach utilizes existing…
Feature EngineeringTransfer LearningGeneralization in birdsong classification: impact of transfer learning methods and dataset characteristics
Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers stil…
Knowledge DistillationSound ClassificationTransfer Learning