<p>Perhaps look at Stowell et al (2019) &quot;Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge&quot; Methods in Ecology &amp; Evolution <a href="https://besjournals.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.13103" rel="noreferrer">https://besjournals.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.13103</a></p>
<p>From the Abstract:</p>
<ol start="3">
<li><p>Multiple methods were able to attain performance of around 88% area under the receiver operating characteristic (ROC) curve (AUC), much higher performance than previous general-purpose methods.</p>
</li>
<li><p>With modern machine learning, including deep learning, general-purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data, with no manual recalibration, and no pretraining of the detector for the target species or the acoustic conditions in the target environment.&quot;</p>
</li>
</ol>
<hr />
<p>The paper discusses four datasets used in the challenge, and according to the data accessibility statement two of them are available, quote:</p>
<p>Both audio and annotations are available under CC-BY-4.0 licences:</p>
<p>warblrb10kaudio: <a href="https://archive.org/details/warblrb10k_public" rel="noreferrer">https://archive.org/details/warblrb10k_public</a></p>
<p>ff1010bird audio: <a href="https://archive.org/details/ff1010bird" rel="noreferrer">https://archive.org/details/ff1010bird</a></p>
<p>Annotations: <a href="https://doi.org/10.6084/m9.figshare.3851466.v1" rel="noreferrer">https://doi.org/10.6084/m9.figshare.3851466.v1</a>.</p>
<hr />
<p>Stowell et al (2019) has also been <a href="https://scholar.google.com/scholar?oi=bibs&amp;hl=en&amp;cites=50270686932665232&amp;as_sdt=5" rel="noreferrer">cited 190 times according to Google Scholar</a>, so I'm sure there are other leads to follow there too.</p>

CC BY-SA 4.0
2022-07-08T08:01:14.263Z
2022-07-08T08:01:14.263Z
EcologyTom
<p>Which publicly available terrestrial bioacoustics dataset (avian ~0-15 kHz) has been most commonly used to benchmark model-based (any functional estimator probabilistic or machine/deep learning) audio classification accuracy (or other metrics derived from a contingency table such as precision, recall, etc...)?</p>

CC BY-SA 4.0
2022-07-06T17:31:04.120Z
Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
Community
CC BY-SA 4.0
2022-07-06T18:21:54.213Z
Welcome @MattWeldy could you add a bit to your question to narrow the scope a but? Are you looking for a specific radon? In air or underwater?
selene
<benchmarks>
Benchmarked bioacoustic dataset
# Benchmarked bioacoustic dataset

(Asked by: username_0 on 2022-07-06)

Which publicly available terrestrial bioacoustics dataset (avian ~0-15 kHz) has been most commonly used to benchmark model-based (any functional estimator probabilistic or machine/deep learning) audio classification accuracy (or other metrics derived from a contingency table such as precision, recall, etc...)?

**Tags:** benchmarks

> **Comments:**
> **username_1**: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.  
> **username_2**: Welcome @username_0 could you add a bit to your question to narrow the scope a but? Are you looking for a specific radon? In air or underwater?  

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## 1 answers

### Answer by username_3 on 2022-07-08. Score: 5

Perhaps look at Stowell et al (2019) "Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge" Methods in Ecology & Evolution <https://besjournals.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.13103>

From the Abstract:

3. Multiple methods were able to attain performance of around 88% area under the receiver operating characteristic (ROC) curve (AUC), much higher performance than previous general-purpose methods.
4. With modern machine learning, including deep learning, general-purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data, with no manual recalibration, and no pretraining of the detector for the target species or the acoustic conditions in the target environment."

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The paper discusses four datasets used in the challenge, and according to the data accessibility statement two of them are available, quote:

Both audio and annotations are available under CC-BY-4.0 licences:

warblrb10kaudio: <https://archive.org/details/warblrb10k_public>

ff1010bird audio: <https://archive.org/details/ff1010bird>

Annotations: <https://doi.org/10.6084/m9.figshare.3851466.v1>.

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Stowell et al (2019) has also been [cited 190 times according to Google Scholar](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=50270686932665232&as_sdt=5), so I'm sure there are other leads to follow there too.


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