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Andrew Farnsworth

5 accepted papers

2020

Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers

ICASSP 2020accepted

Class imbalance in the training data hinders the generalization ability of machine listening systems. In the context of bioacoustics, this issue may be circumvented by aggregating species labels into super-groups of higher taxonomic rank: genus, family, order, and so forth. However, different applic…

Cited by 0SourceScholar
2020

Learning the Helix Topology of Musical Pitch

ICASSP 2020accepted

To explain the consonance of octaves, music psychologists represent pitch as a helix where azimuth and axial coordinate correspond to pitch class and pitch height respectively. This article addresses the problem of discovering this helical structure from unlabeled audio data. We measure Pearson corr…

Cited by 0SourceScholar
2018

Birdvox-Full-Night: A Dataset and Benchmark for Avian Flight Call Detection

ICASSP 2018accepted

This article addresses the automatic detection of vocal, nocturnally migrating birds from a network of acoustic sensors. Thus far, owing to the lack of annotated continuous recordings, existing methods had been benchmarked in a binary classification setting (presence vs. absence). Instead, with the…

Cited by 0SourceScholar
2017

Fusing shallow and deep learning for bioacoustic bird species classification

ICASSP 2017accepted

Automated classification of organisms to species based on their vocalizations would contribute tremendously to abilities to monitor biodiversity, with a wide range of applications in the field of ecology. In particular, automated classification of migrating birds' flight calls could yield new biolog…

Cited by 0SourceScholar