# Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity

Matthew Weldy, Damon Lesmeister, Tom Denton, Adam Duarte, Ben Vernasco, Amandine Gasc, Jennifer Rowe, Michael Adams, Matthew Betts

► To cite this version:

Matthew Weldy, Damon Lesmeister, Tom Denton, Adam Duarte, Ben Vernasco, et al. Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity. Methods in Ecology and Evolution, 2025, 17 (2), pp.322-338. 10.1111/2041-210X.70089. hal-05179716

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# Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity

Matthew J. Weldy<fnref n="1" />,²,³ ID | Damon B. Lesmeister² ID | Tom Denton⁴ ID | Adam Duarte⁵ ID |

Ben J. Vernasco³,⁵,⁶ ID | Amandine Gasc⁷ ID | Jennifer C. Rowe⁸ ID | Michael J. Adams⁸ ID |

Matthew G. Betts<fnref n="1" /> ID

**Correspondence**

Matthew J. Weldy

Email: [matthewjweldy@gmail.com](mailto:matthewjweldy@gmail.com)

**Funding information**

USDA Forest Service Pacific Northwest
Research Station; Oregon State
University Department of Forest
Ecosystems and Society; USDA Forest
Service Collaborative Forest Landscape
Restoration Program (CFLRP)

Handling Editor: Sara Beery

**Abstract**

1. The biodiversity crisis necessitates spatially extensive methods to monitor multiple taxonomic groups for evidence of change in response to evolving environmental conditions. Programs that combine passive acoustic monitoring and machine learning are increasingly used to meet this need. These methods require large, annotated datasets, which are time-consuming and expensive to produce, creating potential barriers to adoption in data- and funding-poor regions. Recently released pre-trained avian acoustic classification models provide opportunities to reduce the need for manual labelling and accelerate the development of new acoustic classification algorithms through transfer learning. Transfer learning is a strategy for developing algorithms under data scarcity that uses pre-trained models from related tasks to adapt to new tasks.

2. Our primary objective was to develop a transfer learning strategy using the feature embeddings of a pre-trained avian classification model to train custom acoustic classification models in data-scarce contexts. We used three annotated avian acoustic datasets to test whether transfer learning and soundscape simulation-based data augmentation could substantially reduce the annotated training data necessary to develop performant custom acoustic classifiers. We also conducted a sensitivity analysis for hyperparameter choice and model architecture. We then assessed the generalizability of our strategy to increasingly novel non-avian classification tasks.

3. With as few as two training examples per class, our soundscape simulation data augmentation approach consistently yielded new classifiers with improved performance relative to the pre-trained classification model and transfer learning

<footnote n="1">¹Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, USA; ²Pacific Northwest Research Station, USDA Forest Service, Corvallis, Oregon, USA; ³Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA; ⁴Google DeepMind, Oakland, California, USA; ⁵Pacific Northwest Research Station, USDA Forest Service, Olympia, Washington, USA; ⁶Whitman College, Department of Biology, Walla Walla, Washington, USA; ⁷Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale, Marseille, France and ⁸U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, Corvallis, Oregon, USA</footnote>
classifiers trained with other augmentation approaches. Performance increases were evident for three avian test datasets, including single-class and multi-label contexts. We observed that the relative performance among our data augmentation approaches varied for the avian datasets and nearly converged for one dataset when we included more training examples.

4. We demonstrate an efficient approach to developing new acoustic classifiers leveraging open-source sound repositories and pre-trained networks to reduce manual labelling. With very few examples, our soundscape simulation approach to data augmentation yielded classifiers with performance equivalent to those trained with many more examples, showing it is possible to reduce manual labeling while still achieving high-performance classifiers and, in turn, expanding the potential for passive acoustic monitoring to address rising biodiversity monitoring needs.

### KEYWORDS

amphibian sounds, avian sounds, biodiversity monitoring, data scarcity, few-shot learning, mammal sounds, orthopteran sounds, passive acoustic monitoring

# 1 | INTRODUCTION

Passive acoustic monitoring (PAM) is an efficient and non-invasive sensor-based sampling approach that can be used to simultaneously collect data for multiple species (Shonfield & Bayne, 2017). However, large data volumes and challenges associated with correctly identifying target sounds have hindered the widespread adoption of PAM (Gibb et al., 2019; Hartig et al., 2023). Recent advances in machine learning offer a promising path towards semi-automated detection and classification of animal vocalizations (Stowell, 2022); however, these algorithms require iterative training over large annotated datasets, which take substantial resources, effort and specialized knowledge to construct. Building training datasets is a significant challenge for new PAM programmes, especially in highly biodiverse regions where conservation needs are high, but financial resources for labelling species vocalizations are low (Cui et al., 2023).

When faced with these challenges, PAM managers typically adopt one of three approaches based on project objectives and available resources. First, project support staff can exhaustively search PAM recordings for target vocalizations. Second, projects may use existing classification and detection algorithms, such as BirdNET (Kahl et al., 2021), Perch (Ghani et al., 2023) or PNW-Cnet (Ruff et al., 2023), and dedicate time to reviewing predictions and calibrating outputs. This involves fine-tuning species-specific detection thresholds and adjusting sampling protocols to manage the risk of false-positive and false-negative predictions (Cole et al., 2022; Wood & Kahl, 2024). Third, some projects may invest considerable resources to annotate acoustic files and train custom local classification algorithms from scratch (Gaylord et al., 2023; Ruff et al., 2023).

Alternatively, PAM managers can adopt an intermediate transfer learning approach that leverages both pre-trained models and

local data to iteratively develop custom acoustic classifiers tailored to specific project needs. Transfer learning is a machine learning technique that adapts the knowledge from pre-trained models to improve performance on related tasks (Pan & Yang, 2010). A common approach to transfer learning uses the penultimate layer output from a pre-trained model—an ‘embedding’—as input to train a new classifier for a different task (Oquab et al., 2014). This approach has been used extensively in computer vision tasks (Kornblith et al., 2019), acoustic classification (Kong et al., 2020) and natural language processing (Houlsby et al., 2019). In ecological acoustic classification, transfer learning has recently shown promise (Ghani et al., 2023; Incze et al., 2018; Nolasco et al., 2023), and pre-trained acoustic classification models like BirdNET (Kahl et al., 2021) and Perch (Ghani et al., 2023) offer strong pre-trained model options. For example, Dufourq et al. (2022) used pre-trained image classification models to develop new acoustic classifiers for Hainan gibbon (Nomascus hainanus), black-and-white ruffed lemur (Varecia variegata), thyolo alethe (Chamaethylas choloensis) and pin-tailed whydah (Vidua macroura). Transfer learning remains underutilized in PAM due to its novelty in ecology, perceived complexity and the misperception that substantial training data are required; however, improved transfer learning strategies could potentially shorten the development cycle and lead to increased adoption of active learning frameworks (Zhao et al., 2020).

Here, we test whether simulated soundscape data augmentation can be used with transfer learning-based acoustic classifiers to substantially reduce training data requirements. We used three annotated avian acoustic datasets, ranging in complexity from 1 to 10 classes. We compared transfer learning-based classifier performance under different model architectures, explored a range of hyperparameter choices and demonstrated that the simulated soundscape approach outperforms simpler data augmentation techniques. We
test the performance of our custom acoustic classifiers relative to the baseline classification performance of two pre-trained avian classification models, BirdNET version 2.4 which targets a global set of species (Kahl et al., 2021) and PNW-Cnet version 4 a local model developed to support Northwest Forest Plan PAM in the Pacific Northwest, USA (Lesmeister & Jenkins, 2022; Ruff et al., 2023). Last, we explore the generalizability of our approach with three non-avian annotated acoustic datasets, ranging in complexity from 1 to 11 classes that vary in overlap with BirdNET's training data.

## 2 | MATERIALS AND METHODS

### 2.1 | Datasets

We assembled six acoustic evaluation datasets, independent of training data for BirdNET version 2.4, ranging in complexity from a single-class classification problem to an 11-class multi-label classification problem. Three datasets, which we refer to as avian datasets, were used to develop strategies for transfer learning under data scarcity (Table 1). The other three datasets, which we refer to as non-avian datasets, were used to explore the generalizability of our strategies when applied to non-avian taxa. The evaluation datasets

were all annotated at the recording level (e.g. one annotation per recording that indicates the presence of at least one vocalization from a species). All the audio examples were resampled to 48kHz using Librosa (version 0.10.0; McFee et al., 2023) and, in some cases, reduced from stereo to mono.

#### 2.1.1 | Avian datasets

We downloaded recordings of our avian target species from XenoCanto spatially filtered to the Pacific Northwest Region between latitudes from 37.730 to 49.030°N and longitudes ranging from 125.000 to 120.500°W, overlapping the sampling area of the PAM datasets. The XenoCanto recordings are focal recordings that feature specific target species and do not typically differentiate calls from songs or annotate background non-target species sounds (Figure 1a; van Merriënboer et al., 2024; Vellinga & Planque, 2015). In addition, the XenoCanto recordings are annotated at the recording level, which vary in length from a few seconds to multiple minutes. We annotated the XenoCanto recordings using predefined target vocalizations (Table 1; Vellinga & Planque, 2015). We then developed two independent annotated evaluation datasets for each avian dataset: one with 3-s sound

<table><caption>TABLE 1 Descriptions of the avian evaluation datasets.</caption><thead><tr><th rowspan="2">Dataset</th><th rowspan="2">Species</th><th rowspan="2">Sonotype</th><th rowspan="2">Call description</th><th rowspan="2">No. of examples</th><th colspan="2">No. of evaluation clips</th></tr><tr><th>3s</th><th>12s</th></tr></thead><tbody><tr><td rowspan="2">Marbled murrelet</td><td rowspan="2">Marbled murrelet</td><td>marmur_call_1</td><td>'keer'</td><td>40</td><td>345</td><td>1000</td></tr><tr><td>non-target</td><td></td><td></td><td>216</td><td>1000</td></tr><tr><td rowspan="8">Blue mountains</td><td rowspan="3">Clark's nutcracker</td><td>clanut_call_1</td><td>'kraa'</td><td>53</td><td>100</td><td>387</td></tr><tr><td>clanut_call_2</td><td>'keer'</td><td>39</td><td>100</td><td>62</td></tr><tr><td>clanut_call_3</td><td>'reek'</td><td>41</td><td>100</td><td>1056</td></tr><tr><td rowspan="2">American goshawk</td><td>norgos_call_1</td><td>Screech series</td><td>27</td><td>99</td><td>229</td></tr><tr><td>norgos_call_2</td><td>Wail</td><td>18</td><td>100</td><td>496</td></tr><tr><td rowspan="2">White-headed woodpecker</td><td>whhwoo_call_1</td><td>'pittik'</td><td>69</td><td>100</td><td>1034</td></tr><tr><td>non-target</td><td></td><td></td><td>0</td><td>0</td></tr><tr><td rowspan="12">Passerine</td><td rowspan="2">Hermit thrush</td><td>herthr_song_1</td><td>Song</td><td>42</td><td>137</td><td>517</td></tr><tr><td>herthr_call_2</td><td>Whine</td><td>36</td><td>8</td><td>62</td></tr><tr><td rowspan="2">Olive-sided flycatcher</td><td>olsfly_song_1</td><td>Song</td><td>50</td><td>128</td><td>114</td></tr><tr><td>olsfly_call_1</td><td>'pip' series</td><td>40</td><td>100</td><td>69</td></tr><tr><td>Spotted towhee</td><td>spotow_song_1</td><td>Song</td><td>278</td><td>22</td><td>125</td></tr><tr><td rowspan="3">Swainson's thrush</td><td>swathr_song_1</td><td>Song</td><td>42</td><td>134</td><td>331</td></tr><tr><td>swathr_call_1</td><td>'pwut'</td><td>127</td><td>42</td><td>142</td></tr><tr><td>swathr_call_3</td><td>'wee'</td><td>101</td><td>44</td><td>195</td></tr><tr><td>Varied thrush</td><td>varthr_song_1</td><td>Song</td><td>101</td><td>51</td><td>500</td></tr><tr><td rowspan="2">Wrentit</td><td>wrenti_song_1</td><td>Song</td><td>55</td><td>13</td><td>54</td></tr><tr><td>non-target</td><td></td><td></td><td>33</td><td>2232</td></tr></tbody></table>

Note: Each dataset is described by its name, the included sonotypes, the number of training examples obtained from XenoCanto, and the number of evaluation clips available in both 3-s and 12-s formats.
## (a) Compiling open source vocalizations.

1. Query online repositories (e.g. Xeno Canto) for focal species vocalizations.

<figure><img src="image_3.png" /></figure>

Study area containing focal species from the marbled murrelet, passerine, and small mammal datasets

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

Open source database

<figure><img src="image_6.png" /></figure>

2. Create and compile fine-scale annotations of recordings.

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Fine-scale annotation of recordings

## (b) Four training data strategies

### 3-Second Raw Clips

3-second windows are established around randomly selected annotations.

<figure><img src="image_8.png" /></figure>

### Embedding Augmentation

Augmented clips created by modifying embeddings with values randomly drawn from a normal distribution.

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

### Timeshift Clips

Augmented clips created (augmented) by timeshifting up to 0.5 seconds around each recording.

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

### Simulated Clips

Simulated soundscapes created by combining source separated target clips and simulated backgrounds.

<figure><img src="image_13.png" /></figure>

* Embeddings used for transfer learning

**FIGURE 1** Conceptual representation of (a) our training data collection and (b) our training data construction approaches including training on few raw examples ('raw'), augmenting the raw examples with standard Gaussian noise ('embedding'), time shifting ('timeshift') and soundscape simulation ('simulated clips').

clips and the other with 12-s sound clips (Table 1). The source files for both datasets were independent recordings but originated from the same PAM projects.

The marbled murrelet (*Brachyramphus marmoratus*; eBird code: marmur) dataset is a single-class classification problem that includes 0.46 h of annotated 3-s clips and 6.67 h of annotated 12-s

clips. The recordings were collected and annotated by the United States Department of Agriculture (USDA) Forest Service in association with the annual Northwest Forest Plan (NWFP) PAM of northern spotted owl (*Strix occidentalis caurina*; eBird code: stroca) populations and other old-forest-associated species (Duarte, Weldy, et al., 2024; Lesmeister & Jenkins, 2022). The recordings
are selected clips from a larger 644,111-h acoustic dataset collected from March to September 2020. During 2020, PAM occurred on 1494 US Federally managed forest sites west of the Cascade Mountain Range in Oregon and Washington, USA. The acoustic data were collected using Song Meter SM4 autonomous recording units (hereafter ARU) at a sampling rate of 32 kHz and a 16-bit resolution.

The Blue Mountains dataset is a six-class multi-label classification problem that includes annotated vocalizations for three species that are either an indicator of forest management activities or are of conservation concern (Altman & Bresson, 2017): Clark's nutcracker (*Nucifraga cumbiana*; eBird code: clanut), American goshawk (*Accipiter atricapillus*; eBird code: norgos) and white-headed woodpecker (*Dryobates albolarvatus*; eBird code: whhwoo). The six-class labels identify three Clark's nutcracker call types, two American goshawk call types and one white-headed woodpecker call type. The recordings were collected by the USDA Forest Service in Oregon, USA, in the northern Blue Mountains on the Wallowa-Whitman and Umatilla National Forests as part of ongoing PAM for the Northern Blues Collaborative Forest Landscape Restoration Program (Duarte, Vernasco, et al., 2024; https://research.fs.usda.gov/pnw/understory/northern-blue-mountains-wildlife-monitoring-2022-2023). The recordings are selected clips from a larger 122,052-h dataset collected on 420 US Federally managed forest sites. The acoustic data were collected using Song Meter SM4 ARUs at a sampling rate of 32 kHz and a 16-bit resolution (Wildlife Acoustics, Concord, NY, U. S. A.).

The passerine dataset is a 10-class multi-label classification problem that includes annotated vocalizations for six species: hermit thrush (*Catharus guttatus*; eBird code: herthr), olive-sided flycatcher (*Contopus cooperi*; eBird code: olsfly), spotted towhee (*Pipilo maculatus*; eBird code: spotow), Swainson's thrush (*Catarus ustulatus*; eBird code: swathr), varied thrush (*Ixoreus naevius*; eBird code: varthr) and wrentit (*Chamaea fasciata*; eBird code: wrenti). The six-class labels identify three Swainson's thrush call types, two hermit thrush call types, two olive-sided flycatcher call types, one spotted towhee call type, one varied thrush call type and one wrentit call type. These recordings were obtained from Weldy et al. (2024) and included a subset of dawn chorus recordings collected during the 2022 NWFP PAM, described above. In 2022, PAM expanded to include 2572 US Federally managed forest sites west of the Cascade and Sierra Mountain Ranges in California, Oregon and Washington, recording 1,477,751.64 h of sound. The sampling protocol and acoustic characteristics were consistent with those described in the marbled murrelet dataset.

## 2.1.2 | Non-avian datasets

The non-avian datasets are multi-label classification problems that represent a range of classification complexities, varying in number of target classes and the degree to which the evaluation data differs from BirdNET's base training data.

The amphibian dataset includes 2494 5-s recordings annotated for two amphibian species: American bullfrog (*Lithobates catesbeianus*; code: amebul) and Pacific chorus frog (*Pseudacris regilla*; code: pacfro). We obtained training examples from California Herps, a web resource documenting the life history of California's reptiles and amphibians (Nafis, 2021; acoustic data obtained with permission). The annotated evaluation data were collected by the United States Geological Survey (USGS) and its partners at 86 wetland sites across Oregon and Washington, USA (Hill et al., 2019). The objective was to monitor bullfrog vocalization activity in the range of federally threatened Oregon spotted frogs (*Rana pretiosa*); non-target Pacific chorus frogs also occurred in the study areas. The recordings were collected using AudioMoth ARUs at a sampling rate of 48 kHz in 2020 and 16 kHz in 2021. The annotated clips were identified using the Kaleidoscope software cluster analysis feature targeting the bullfrog call's frequency range (187.5–5250 Hz; Bielinski et al., 2020) and manually reviewed. Audio collection by USGS was covered under annual USFWS Special Use Permits 20-04 and 21-01.

The cricket dataset includes 1000 2-min recordings annotated for 10 species of cricket and one cricket subfamily: *Archenopterus bouensis*, *Bullita fusca*, *Bullita mouirangensis*, *Bullita obscura*, *Calscirtus magnus*, *Koghiella flammea*, *Koghiella nigris*, *Notosciobia affnis* *paranola*, *Notoscioba minoris*, *Pseudotrigonidium caledonica* and *Trigonidiinae* spp. We obtained training examples from the Muséum National d'Histoire Naturelle of Paris (https://sonotheque.mnhn.fr; Sound Catalog accessed 8/01/2024). The annotated PAM data used for evaluation were recorded in New Caledonia as a component of long-term research on the effects of the invasive little fire ant *Wasmannia auropunctata* on biodiversity (Jourdan et al., 2001). Gasc et al. (2018) collected PAM recordings on 24 forest, pre-forest and shrubland sites during the dry season of 2013 and used these recordings to assess acoustic-based detection of *Wasmannia auropunctata* through changes in the acoustic calling behaviour of crickets. The recordings were collected using SongMeter SM2 and SM2+ ARUs at a sampling rate of 48 kHz and a 16-bit resolution (Wildlife Acoustics, Concord, NY, USA). We used two versions of this dataset: the first includes 11 classes, treating species-level classes separately. The second includes seven classes pooling species-level annotations at the taxonomic resolution of genus. We created the second version of this dataset to create a potentially easier classification task because we suspected that the first task would be a difficult out of domain task for BirdNET.

The small mammal dataset includes 1737 12-s recordings annotated for three sounds from two species: American pika (*Ochotona pinceps*; code: amepik) and Douglas squirrel (*Tamiasciurus douglasii*; code: dousqu). The three class labels identify two Douglas squirrel vocalizations and one American pika vocalization. The recordings were collected and annotated by the USDA Forest Service during NWFP PAM, described above. The sampling protocol and acoustic characteristics were consistent with those described in the marbled murrelet dataset.
3 | RESULTS

We found that training custom acoustic classifiers with as few as two positive examples improves classifier performance relative to BirdNET's off-the-shelf predictions (Experiment 2; Figure 2). For all three avian datasets, average relative performance increased asymptotically as the number of training source examples increased, while the variance of the average relative performance among training replicates decreased (Experiment 4; Figure 3; Table S4). Average performance of the classifiers trained with soundscape simulation saturated quickly, with both AUC<sub>macro</sub> and mAP reaching 95% of the maximum performance

for each avian dataset with just four training samples. Of the four approaches to training data construction, the simulated clips approach consistently performed the best and yielded improvements relative to the BirdNET baseline. The other three training data strategies often failed to improve upon the BirdNET baseline, except for the Blue Mountains dataset, where all four strategies resulted in higher AUC<sub>macro</sub> and mAP relative to the baseline. We observed a plateau in model performance gains, similar to other transfer learning applications, where increases in data can show diminishing relative performance improvements (Ghani et al., 2023; Kath et al., 2024). These diminishing gains may reflect inherent limitations to the information content of the

<figure><img src="image_14.png" /><figcaption>FIGURE 2 Relative performance of four training data strategies for developing acoustic classification algorithms using transfer learning under data scarcity. The simulated clips data augmentation approach consistently performed the best and yielded improvements relative to the BirdNET baseline. The number of raw examples varied, and the dataset construction and training were replicated 10 times for 2, 4, 8, 16 and 32 raw examples. Dark points and lines show the average performance of the four training approaches. Light points indicate replicate-level performance. Area under the receiver operating characteristic (ROC) curve (AUC) measures the probability that a randomly selected true positive example is scored higher than a randomly selected true negative example. Average precision (AP) is the weighted mean of precision across all thresholds. AUC<sub>macro</sub> and mAP are the mean of all class-specific metrics. of precision across all thresholds. AUC<sub>macro</sub> and mAP are the mean of all class-specific metrics.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>FIGURE 3 Relative effects of increasing the number of simulated clips and three acoustic data augmentation techniques relative to soundscape simulation without acoustic augmentations for acoustic classification algorithms using transfer learning under data scarcity. Increasing the number of simulated soundscapes consistently improved relative model performance, with no evidence of overfitting. The number of simulated examples varied, and the dataset construction and training were replicated 10 times. Dark points and lines indicate the average performance of the acoustic augmentations.</figcaption></figure>

embeddings, such as limited discriminatory power to differentiate among different sounds produced by the same species (Figure 4), or differences among training and evaluation datasets.

Average classifier performance varied little among the eight classifier architectures for classifiers with up to 10 classes (Experiment 3; Table 3). All four base architectures were competitive, and future studies facing more complex classification problems should evaluate classifier architectures with higher relative capacity. Adding a hidden layer decreased classifier performance relative to the linear classifier, likely due to overfitting. This overfitting was evidenced by continued decreases in training loss over batches of simulated data, even after generalization performance on the evaluation datasets plateaued. However, applying dropout before prediction partially mitigated this decrease in performance, preventing overfitting to

the simulated training data and restoring two-layer model performance closer to the level of the linear classifier.

Increasing the number of simulated soundscapes increased relative model performance, with no evidence for overfitting (Experiment 4; Figure 3). However, the rate of increase in performance was slow at greater than 1000 simulated soundscapes (Figure 3). Adding acoustic augmentations, such as pitch shifting and time stretching, to the positive training examples during the simulations did not increase average relative performance (Figure 3).

Each hyperparameter value was included in the top 10 average hyperparameter combinations at least once, and the relative performance varied across replicate linear classifier fits with the same hyperparameter combination (Experiment 1; Table 2). We found slight evidence of overfitting for the marbled murrelet and Blue Mountains
<figure><img src="image_16.png" /><figcaption>FIGURE 4 t-distributed stochastic neighbour embedding (t-SNE) plots of the 3-s avian evaluation datasets. The t-SNE embeddings were fit for 5000 iterations using a learning rate 10 and a perplexity value 50. Each point on the plots is the 2-dimensional t-SNE projection of the 1024-dimensional BirdNET feature embedding for a 3-s annotated audio clip. The t-SNE visualization illustrates the variability among acoustic clips based on BirdNET feature embeddings. Different colours indicate groups of distinct sound types (sonotypes). See Table 1 for descriptions of sonotypes and corresponding species.</figcaption></figure>

<table><caption>TABLE 2 Hyperparameter ablation for simulation-based transfer learning classifiers built over BirdNET embeddings for the three avian datasets.</caption><thead><tr><th rowspan="2">Hyperparameter</th><th rowspan="2">Value</th><th colspan="2">Marbled murrelet</th><th colspan="2">Blue mountains</th><th colspan="2">Passerine</th></tr><tr><th>AUC</th><th>AP</th><th>AUC<sub>macro</sub></th><th>mAP</th><th>AUC<sub>macro</sub></th><th>mAP</th></tr></thead><tbody><tr><td rowspan="4">Batch size</td><td>16</td><td>2</td><td>2</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>32</td><td>1</td><td>2</td><td>2</td><td>3</td><td>2</td><td>3</td></tr><tr><td>64</td><td>3</td><td>3</td><td>3</td><td>3</td><td>2</td><td>2</td></tr><tr><td>128</td><td>4</td><td>3</td><td>3</td><td>4</td><td>4</td><td>5</td></tr><tr><td rowspan="3">Learning rate</td><td>0.001</td><td>2</td><td>0</td><td>4</td><td>0</td><td>0</td><td>3</td></tr><tr><td>0.01</td><td>7</td><td>8</td><td>4</td><td>3</td><td>2</td><td>7</td></tr><tr><td>0.1</td><td>1</td><td>2</td><td>2</td><td>7</td><td>8</td><td>0</td></tr><tr><td rowspan="4">Training steps</td><td>100</td><td>2</td><td>1</td><td>5</td><td>5</td><td>5</td><td>0</td></tr><tr><td>500</td><td>0</td><td>0</td><td>5</td><td>4</td><td>1</td><td>2</td></tr><tr><td>1000</td><td>4</td><td>4</td><td>0</td><td>1</td><td>2</td><td>3</td></tr><tr><td>2000</td><td>4</td><td>5</td><td>0</td><td>0</td><td>2</td><td>5</td></tr></tbody></table>

Note: We varied batch size, learning rate and the number of gradient descent steps. The value indicates the number of times the average performance in terms of area under the curve (AUC) and average precision (AP) for a specific hyperparameter value was in the top 10 average hyperparameter combinations. Area under the ROC curve (AUC) measures the probability that a randomly selected true positive example is scored higher than a randomly selected true negative example. Average precision (AP) is the weighted mean of precision across all thresholds. AUC<sub>macro</sub> and mAP are the mean of all class-specific metrics. Bold values indicate the top-performing value for a specific hyperparameter, metric and avian dataset. Bold and italic values indicate shared top-performing values for a specific hyperparameter.

classifiers because model performance estimates across the set of hyperparameter combinations were within a range of 0.05 (Table S3). The hyperparameter evaluation provided weak evidence that larger batch sizes and intermediate learning rates marginally improved relative performance. We adopted the following hyperparameter values for the remainder of our analyses: batch size: 128, learning rate: 0.01 and 500 gradient descent steps. Our choice for the number of gradient descent steps represents a compromise between reducing the tendency for overfitting while training long enough for the model to be exposed to all the data at least once.

Overall, BirdNET and PNW-Cnet performed well on the avian evaluation datasets. PNW-Cnet AUC and AP scores were higher than BirdNET for 80% of the shared acoustic classes (n=10; Table 4). The average class-specific performance of the simulation-based classifiers trained using four known examples was higher than BirdNET for all classes except for two cases where the AP of the simulation-based classifiers did not improve upon BirdNET's performance (Table 4). However, the maximum class-specific performance of the simulation-based classifiers was higher than BirdNET's score for all classes. The average class-specific performance of the
<figure><img src="image_17.png" /></figure>

<table><caption>TABLE 3 Model structure ablation for the three avian datasets.</caption><thead><tr><th rowspan="2">Structure</th><th rowspan="2">No. of units</th><th rowspan="2">Dropout</th><th colspan="2">Marbled murrelet</th><th colspan="2">Blue mountains</th><th colspan="2">Passerine</th></tr><tr><th>AUC</th><th>AP</th><th>AUC<sub>macro</sub></th><th>mAP</th><th>AUC<sub>macro</sub></th><th>mAP</th></tr></thead><tbody><tr><td rowspan="2">LP</td><td rowspan="2"></td><td>N</td><td>0.990</td><td>0.991</td><td>0.955</td><td>0.866</td><td>0.898</td><td>0.602</td></tr><tr><td>Y</td><td>0.990</td><td>0.991</td><td>0.958</td><td>0.872</td><td>0.90</td><td>0.609</td></tr><tr><td rowspan="4">MLP</td><td rowspan="2">1024</td><td>N</td><td>0.934</td><td>0.934</td><td>0.952</td><td>0.861</td><td>0.893</td><td>0.591</td></tr><tr><td>Y</td><td>0.953</td><td>0.938</td><td>0.953</td><td>0.860</td><td>0.897</td><td>0.602</td></tr><tr><td rowspan="2">2048</td><td>N</td><td>0.933</td><td>0.929</td><td>0.953</td><td>0.863</td><td>0.893</td><td>0.591</td></tr><tr><td>Y</td><td>0.933</td><td>0.913</td><td>0.954</td><td>0.862</td><td>0.897</td><td>0.601</td></tr><tr><td rowspan="2">512</td><td rowspan="2"></td><td>N</td><td>0.966</td><td>0.958</td><td>0.951</td><td>0.859</td><td>0.893</td><td>0.590</td></tr><tr><td>Y</td><td>0.974</td><td>0.962</td><td>0.955</td><td>0.865</td><td>0.897</td><td>0.600</td></tr></tbody></table>

Note: We report the average performance for eight classifier model architectures across 10 replicate model trainings. LP refers to a single-layer linear probe. Three two-layer perceptron (MLP) architectures vary in the number of units included in a single hidden layer. Area under the ROC curve (AUC) measures the probability that a randomly selected true positive example is scored higher than a randomly selected true negative example. Average precision (AP) is the weighted mean of precision across all thresholds. AUC<sub>macro</sub> and mAP are the mean of all class-specific metrics.

<table><caption>TABLE 4 Comparative class-specific performance of BirdNET, PNW-Cnet and simulation-based transfer learning classifiers built over BirdNET embeddings and four known examples for shared classes in the 12-s avian evaluation datasets.</caption><thead><tr><th rowspan="2">Sonotype</th><th colspan="4">AUC</th><th colspan="4">AP</th></tr><tr><th>BirdNET</th><th>PNW-Cnet</th><th>Sim<sub>mean</sub></th><th>Sim<sub>max</sub></th><th>BirdNET</th><th>PNW-Cnet</th><th>Sim<sub>mean</sub></th><th>Sim<sub>max</sub></th></tr></thead><tbody><tr><td>marmur_call_1</td><td>0.976</td><td>0.998</td><td>0.988</td><td>0.991</td><td>0.977</td><td>0.999</td><td>0.989</td><td>0.992</td></tr><tr><td>clanut_call_1</td><td>0.782</td><td></td><td>0.958</td><td>0.981</td><td>0.221</td><td></td><td>0.847</td><td>0.915</td></tr><tr><td>clanut_call_2</td><td>0.741</td><td></td><td>0.934</td><td>0.961</td><td>0.084</td><td></td><td>0.713</td><td>0.770</td></tr><tr><td>clanut_call_3</td><td>0.897</td><td>0.843</td><td>0.939</td><td>0.969</td><td>0.736</td><td>0.611</td><td>0.872</td><td>0.945</td></tr><tr><td>norgos_call_1</td><td>0.820</td><td></td><td>0.953</td><td>0.969</td><td>0.320</td><td></td><td>0.840</td><td>0.877</td></tr><tr><td>norgos_call_2</td><td>0.920</td><td></td><td>0.976</td><td>0.985</td><td>0.610</td><td></td><td>0.916</td><td>0.942</td></tr><tr><td>whhwoo_call_1</td><td>0.964</td><td></td><td>0.987</td><td>0.992</td><td>0.956</td><td></td><td>0.980</td><td>0.988</td></tr><tr><td>herthr_song_1</td><td>0.810</td><td>0.928</td><td>0.849</td><td>0.877</td><td>0.570</td><td>0.863</td><td>0.642</td><td>0.709</td></tr><tr><td>herthr_call_2</td><td>0.875</td><td></td><td>0.874</td><td>0.908</td><td>0.190</td><td></td><td>0.589</td><td>0.622</td></tr><tr><td>olsfly_song_1</td><td>0.836</td><td>0.926</td><td>0.902</td><td>0.933</td><td>0.243</td><td>0.673</td><td>0.605</td><td>0.656</td></tr><tr><td>olsfly_call_1</td><td>0.911</td><td></td><td>0.924</td><td>0.959</td><td>0.541</td><td></td><td>0.619</td><td>0.651</td></tr><tr><td>spotow_song_1</td><td>0.888</td><td>0.803</td><td>0.922</td><td>0.951</td><td>0.606</td><td>0.120</td><td>0.540</td><td>0.633</td></tr><tr><td>swathr_song_1</td><td>0.897</td><td>0.971</td><td>0.931</td><td>0.945</td><td>0.484</td><td>0.886</td><td>0.733</td><td>0.759</td></tr><tr><td>swathr_call_1</td><td>0.907</td><td></td><td>0.962</td><td>0.974</td><td>0.541</td><td></td><td>0.745</td><td>0.822</td></tr><tr><td>swathr_call_3</td><td>0.891</td><td></td><td>0.896</td><td>0.920</td><td>0.512</td><td></td><td>0.605</td><td>0.678</td></tr><tr><td>varthr_song_1</td><td>0.833</td><td>0.924</td><td>0.854</td><td>0.871</td><td>0.607</td><td>0.836</td><td>0.656</td><td>0.697</td></tr><tr><td>wrenti_song_1</td><td>0.806</td><td>0.922</td><td>0.847</td><td>0.874</td><td>0.373</td><td>0.637</td><td>0.364</td><td>0.426</td></tr></tbody></table>

Note: We report the average and maximum area under the receiver operator curve (AUC) and average precision (AP) for each class across 10 replicate datasets and model training steps.

simulation-based classifiers only surpassed PNW-Cnet's scores for classes in which BirdNET also scored higher, but the maximum performance of the simulation-based classifier was competitive or surpassed PNW-Cnet for all classes (Table 4).

For the three non-avian datasets, relative performance decreased with increasing task complexity (Table 5). BirdNET's overall baseline performance for the amphibian dataset was high (AUC<sub>macro</sub>: 0.993; mAP: 0.991) with high class-specific performance:

the American bullfrog AUC<sub>macro</sub> was 0.995 and mAP was 0.995, the Pacific chorus frog AUC<sub>macro</sub> was 0.991 and mAP was 0.987. AUC and AP scores for our methods were consistent with, but slightly lower than, BirdNET's baseline performance for these species (Table 5). We could not estimate BirdNET's baseline performance for the cricket and small mammal datasets because the species comprising those datasets are not included in BirdNET's training data. For our approach, the overall performance of the cricket dataset was
<table><caption>TABLE 5 Overall and class-specific performance for three out-of-domain linear classifiers trained with few examples using a simulation-based transfer learning approach.</caption><thead><tr><th>Taxa</th><th>Species</th><th>No. of examples</th><th>No. of evaluations</th><th>AUC</th><th>AP</th></tr></thead><tbody><tr><td rowspan="4">Amphibian</td><td>Bullfrog</td><td>45</td><td>965</td><td>0.996</td><td>0.995</td></tr><tr><td>Pacific chorus frog</td><td>51</td><td>912</td><td>0.994</td><td>0.992</td></tr><tr><td>Non-target</td><td></td><td>895</td><td></td><td></td></tr><tr><td>Overall</td><td></td><td></td><td>0.995</td><td>0.994</td></tr><tr><td rowspan="10">Cricket-Genus</td><td>Archenopterus</td><td>11</td><td>163</td><td>0.787</td><td>0.538</td></tr><tr><td>Bullita</td><td>13</td><td>132</td><td>0.831</td><td>0.346</td></tr><tr><td>Calscirtus</td><td>1</td><td>221</td><td>0.814</td><td>0.076</td></tr><tr><td>Koghiella</td><td>6</td><td>147</td><td>0.647</td><td>0.252</td></tr><tr><td>Notosciobia</td><td>5</td><td>126</td><td>0.938</td><td>0.870</td></tr><tr><td>Pseudotrigonidium</td><td>6</td><td>29</td><td>0.598</td><td>0.200</td></tr><tr><td>Trigonidiinae</td><td>15</td><td>53</td><td>0.767</td><td>0.239</td></tr><tr><td>Non-target</td><td></td><td>509</td><td></td><td></td></tr><tr><td>Overall</td><td></td><td></td><td>0.769</td><td>0.360</td></tr><tr><td rowspan="15">Cricket-Species</td><td>Archenopterus bouensis</td><td>11</td><td>163</td><td>0.787</td><td>0.540</td></tr><tr><td>Bullita fusca</td><td>6</td><td>88</td><td>0.825</td><td>0.344</td></tr><tr><td>Bullita mouirangensis</td><td>2</td><td>20</td><td>0.811</td><td>0.078</td></tr><tr><td>Bullita obscura</td><td>5</td><td>24</td><td>0.664</td><td>0.260</td></tr><tr><td>Calscirtus magnus</td><td>1</td><td>221</td><td>0.934</td><td>0.863</td></tr><tr><td>Koghiella flammea</td><td>3</td><td>43</td><td>0.598</td><td>0.201</td></tr><tr><td>Koghiella nigris</td><td>3</td><td>104</td><td>0.756</td><td>0.233</td></tr><tr><td>Notosciobia affnis paranola</td><td>2</td><td>105</td><td>0.810</td><td>0.499</td></tr><tr><td>Notosciobia minoris</td><td>3</td><td>21</td><td>0.836</td><td>0.185</td></tr><tr><td>Pseudotrigonidium caledonica</td><td>6</td><td>29</td><td>0.846</td><td>0.179</td></tr><tr><td>Trigonidiinae spp.</td><td>15</td><td>53</td><td>0.683</td><td>0.171</td></tr><tr><td>Non-target</td><td></td><td>509</td><td></td><td></td></tr><tr><td>Overall</td><td></td><td></td><td>0.777</td><td>0.323</td></tr><tr><td rowspan="5">Small mammal</td><td>American pika</td><td>60</td><td>845</td><td>0.970</td><td>0.977</td></tr><tr><td>Douglas squirrel: chirp</td><td>30</td><td>236</td><td>0.959</td><td>0.876</td></tr><tr><td>Douglas squirrel: rattle</td><td>30</td><td>157</td><td>0.915</td><td>0.774</td></tr><tr><td>Non-target</td><td></td><td>515</td><td></td><td></td></tr><tr><td>Overall</td><td></td><td></td><td>0.948</td><td>0.876</td></tr></tbody></table>

Note: We report the dataset properties, as well as overall and class-specific average area under the receiver operator curve (AUC) and average precision (AP) for the amphibian, cricket and small mammal datasets.

moderate (AUC<sub>macro</sub>: 0.777; mAP: 0.323) and improved slightly after aggregating species-level classes by genus (AUC<sub>macro</sub>: 0.769; mAP: 0.360). For the species-level classifier, overall performance was lowered by the poor performance of *the Bullita obscura* and *Koghiella flammea* classes (Table 5). Overall performance on the mammalian dataset was strong (AUC<sub>macro</sub>: 0.948; mAP: 0.876), with all three classes scoring high in terms of AUC and AP (Table 5).

## 4 | DISCUSSION

The recent release of pre-trained avian classification models marks an important advancement for PAM. These models offer

ready-to-use acoustic detection and classification for many vocalizing species (Kahl et al., 2021) and strong foundations for developing custom acoustic classifiers using transfer learning. Here, we demonstrate a low-cost, rapid computational workflow that leverages pre-trained models to develop custom acoustic classifiers with as few as two vocalization examples. The performance of our custom acoustic classifiers typically exceeds the off-the-shelf performance of pre-trained models targeting global sets of species and approaches the performance of specialized pre-trained local classifiers that may take years and substantial investment to build (Gibb et al., 2019). This workflow reduces reliance on large annotated datasets, expediting the time it takes to transform acoustic data into ecological insights, potentially increasing stakeholder
<figure><img src="image_18.png" /></figure>

<table><caption>TABLE 6 Suggested transfer learning strategy for training custom acoustic classification models.</caption><thead><tr><td>Consideration</td><td>Experiment</td><td>Finding</td><td>Suggestion</td></tr></thead><tbody><tr><td>Hyperparameter values</td><td>1</td><td>There were multiple competitive combinations of hyperparameter values</td><td>We suggest starting with large batch sizes, moderate learning rates and short training schedules. However, project-specific hyperparameter ablations may yield marginal relative performance gains</td></tr><tr><td>Data augmentation</td><td>2</td><td>Soundscape simulation consistently improved performance, while other augmentations had no effect</td><td>In data-scarce contexts, use soundscape simulation</td></tr><tr><td>Model architecture</td><td>3</td><td>Single-layer linear classifiers were surprisingly robust for all three avian datasets. However, applying a dropout prior to prediction marginally improve relative performance</td><td>Use a linear classifier directly on pre-trained embeddings. Apply dropout on the embeddings during training</td></tr><tr><td>Acoustic augmentations</td><td>4</td><td>Additional acoustic augmentations, such as pitch shifting and time stretching, did not increase model performance and slowed down the simulation process</td><td>Avoid adding additional acoustic augmentations to the soundscape simulation</td></tr><tr><td>Increasing the number of simulated clips</td><td>4</td><td>Increasing the number of simulated clips when using soundscape simulation augmentation marginally improved model performance, but the relative gains were slow after 4000 clips</td><td>Simulating at least ~4000 clips, increasing the number when practical, especially for multi-label tasks</td></tr></tbody></table>

Note: We report our findings and suggestions for five transfer learning model training considerations.

engagement and the conservation impact (Makiola et al., 2020; Weiskopf et al., 2022).

Transfer learning is a promising approach for adapting pre-trained foundational models to local problems. This study provides a transfer learning strategy for developing custom acoustic classifiers (Table 6). Simple linear classifiers trained on supervised embeddings are a robust approach for developing custom classifiers to improve performance for in-domain sounds, adapt species-level predictions to within-species sound types, or classify novel sounds (Ghani et al., 2023; Kath et al., 2024). We found that the performance of simple linear classifiers trained on raw examples improved by training on simulated soundscapes, while other augmentations included in this study failed to produce consistent improvements over the baseline (Figure 2).

Adopting a transfer learning approach that leverages pre-trained classification models allows new and ongoing PAM programmes to mitigate the risks associated with developing computational processing tools by shortening the time between model training and performance feedback. This shortened feedback loop allows PAM programmes to quickly incorporate new monitoring targets or respond to changing environmental conditions. Additionally, the shorter development cycle facilitates the use of an active learning framework (Zhao et al., 2020). In an active learning framework, users start with a simple linear classifier and iteratively develop an informative local training dataset through model training, prediction and review cycles—training both ‘what is’ and ‘what is not’ an acoustic target (Williams et al., 2024). In our analysis, a local classification model had higher class-specific performance than a global model’s off-the-shelf predictions for eight of 10 shared classes, revealing room for global models to improve when adapted to local problems. But in all these cases, our soundscape simulation-based transfer

learning classifier, trained with a few examples, substantially narrowed the gap in performance between these two pre-trained models, and the transfer learning model will likely continue to improve after exposure to more annotated local data.

Our method is applicable to other pre-trained embedding models, including other wildlife-focused acoustic models like Perch (Ghani et al., 2023) and PNW-Cnet (Ruff et al., 2023), as well as general-purpose acoustic classification (Hershey et al., 2017; Kong et al., 2020). Embedding models map acoustic training datasets to numeric embeddings, with differences in training datasets reflected in the information content of the embeddings (Turian et al., 2022). Consequently, the effectiveness of transfer learning depends on the chosen embedding model, particularly when the target data differ from the training dataset (Williams et al., 2024). For instance, BirdNET’s species-level training may cause its feature space to collapse dissimilar acoustic sounds from the same species into similar representations, limiting the utility of its embeddings to distinguish among call types within a species (Figure 4). Transfer learning applications will likely perform better when embedding models are selected based on project-specific factors, such as similarities between the training and sample data domains, alignment of model context window length with vocalization duration or the model’s receptive frequency range with the target vocalizations. Nonetheless, other embedding models should be considered for complex problems, as they may yield different performance outcomes. However, special care should be given to ensure that evaluation datasets for transfer learning tasks are independent of both the transfer learning classifier’s training data and that of the embedding model.

There is potential for our approach to extend beyond the original training scope of BirdNET (Table 5), enabling PAM programmes to rapidly adapt pre-trained global classifiers to local monitoring
<figure><img src="image_19.png" /></figure>

## CONFLICT OF INTEREST STATEMENT

The authors have no conflict of interest to declare.

## PEER REVIEW

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210x.70089.

## DATA AVAILABILITY STATEMENT

Evaluation data are available via https://doi.org/10.5281/zenodo.15578980 (Weldy et al., 2025). Python scripts, an environment.yml, Dockerfile and compressed docker image used to execute the analyses are also included in a compressed '.zip' folder via Zenodo at the same link (Weldy et al., 2025). The environment.yml file describes the required Python package dependencies, and the Dockerfile preserves the computational environment. We also include a general-purpose Python script (11_new_applications.py) and a vignette describing its use to develop novel classifiers beyond the experimental case-studies.

## ORCID

Matthew J. Weldy ID https://orcid.org/0000-0002-8627-2466

Damon B. Lesmeister ID https://orcid.org/0000-0003-1102-0122

Tom Denton ID https://orcid.org/0000-0003-3866-0031

Adam Duarte ID https://orcid.org/0000-0003-0034-1764

Ben J. Vernasco ID https://orcid.org/0000-0002-5561-7273

Amandine Gasc ID https://orcid.org/0000-0001-8369-4930

Jennifer C. Rowe ID https://orcid.org/0000-0002-5253-2223

Michael J. Adams ID https://orcid.org/0000-0001-8844-042X

Matthew G. Betts ID https://orcid.org/0000-0002-7100-2551

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Table S1:** Description of the discrete audio examples from the training and evaluation datasets, including passive acoustic recordings (PAM) and simulated soundscapes (sim).
**Table S2:** Description of BirdNET's embedded values of the training and evaluation datasets, including passive acoustic recordings (PAM) and simulated soundscapes (sim).

**Table S3:** Hyperparameter ablation for simulation-based transfer learning classifiers built over BirdNET embeddings for the three avian datasets.

**Table S4:** Standard deviation (σ) of macro averaged Area Under the ROC curve and mean Average Precision among ten replicate trainings for four training data strategies for developing acoustic classification algorithms using transfer learning under data scarcity.

**Figure S1:** t-distributed stochastic neighbor embedding (t-SNE) plots of the 3-s avian evaluation datasets demonstrating the effect of increasing the perplexity value.

**How to cite this article:** Weldy, M. J., Lesmeister, D. B., Denton, T., Duarte, A., Vernasco, B. J., Gasc, A., Rowe, J. C., Adams, M. J., & Betts, M. G. (2026). Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity. *Methods in Ecology and Evolution*, 17, 322–338. https://doi.org/10.1111/2041-210x.70089