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Exploring Meta Information for Audio-based Zero-shot Bird Classification
Sound · Submitted 15 Sep 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The best results are achieved by concatenating the AVONET and BLH features attaining a mean unweighted F1-score of .233 over five different test sets with 8 to 10 classes.
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The abstract, sentence by sentence
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research. Nevertheless, data scarcity is still an issue for rare and underrepresented species. This study investigates how meta-information can improve zero-shot audio classification, utilising bird species as an example case study due to the availability of rich and diverse meta-data. We investigate three different sources of metadata: textual bird sound descriptions encoded via (S)BERT, functional traits (AVONET), and bird life-history (BLH) characteristics. As audio features, we extract audio spectrogram transformer (AST) embeddings and project them to the dimension of the auxiliary information by adopting a single linear layer. Then, we employ the dot product as compatibility function and a standard zero-shot learning ranking hinge loss to determine the correct class. The best results are achieved by concatenating the AVONET and BLH features attaining a mean unweighted F1-score of .233 over five different test sets with 8 to 10 classes.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research. | background 1.00 | 0.05 | 0.01 | asserted 1.00 |
| s1 | Nevertheless, data scarcity is still an issue for rare and underrepresented species. | gap 0.71 | 0.06 | 0.02 | asserted 1.00 |
| s2 | This study investigates how meta-information can improve zero-shot audio classification, utilising bird species as an example case study due to the availability of rich and diverse meta-data. | contribution 1.00 | 0.57 | 0.04 | asserted 0.74 |
| s3 | We investigate three different sources of metadata: textual bird sound descriptions encoded via (S)BERT, functional traits (AVONET), and bird life-history (BLH) characteristics. | method 0.87 | 0.85 | 0.03 | asserted 0.94 |
| s4 | As audio features, we extract audio spectrogram transformer (AST) embeddings and project them to the dimension of the auxiliary information by adopting a single linear layer. | method 1.00 | 0.94 | 0.02 | asserted 0.99 |
| s5 | Then, we employ the dot product as compatibility function and a standard zero-shot learning ranking hinge loss to determine the correct class. | method 1.00 | 0.90 | 0.03 | asserted 0.97 |
| s6 | The best results are achieved by concatenating the AVONET and BLH features attaining a mean unweighted F1-score of .233 over five different test sets with 8 to 10 classes. | result 1.00 | 0.96 | 0.84 | asserted 1.00 |
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The paper
Original abstract
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research. Nevertheless, data scarcity is still an issue for rare and underrepresented species. This study investigates how meta-information can improve zero-shot audio classification, utilising bird species as an example case study due to the availability of rich and diverse meta-data. We investigate three different sources of metadata: textual bird sound descriptions encoded via (S)BERT, functional traits (AVONET), and bird life-history (BLH) characteristics. As audio features, we extract audio spectrogram transformer (AST) embeddings and project them to the dimension of the auxiliary information by adopting a single linear layer. Then, we employ the dot product as compatibility function and a standard zero-shot learning ranking hinge loss to determine the correct class. The best results are achieved by concatenating the AVONET and BLH features attaining a mean unweighted F1-score of .233 over five different test sets with 8 to 10 classes.
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How the literature uses it
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Papers citing it
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Many strategies have been studied for improving the generalisation of the model over different datasets [ref] .
DB3V: A Dialect Dominated Dataset of Bird Vocalisation for Cross-corpus Bird Species Recognition · Introduction · 0.83
What it cites · 1 sentence without a confident label
Cites as background 1 paper
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The visual recognition of avian species has become a standardised benchmark for ZSL, owing to the importance of the problem and the availability of suitable metadata: binary description attributes [ref] , textual descriptions [ref] , field-guide visualisations [ref] , and even DNA data [ref] can all serve as mediating attributes for ZSL.
Recognition of Unseen Bird Species by Learning from Field Guides · Introduction · 0.81
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