AVEX: What Matters for Animal Vocalization Encoding
Authors: Marius Miron
David Robinson
Milad Alizadeh
Ellen Gilsenan-McMahon
Gagan Narula
Emmanuel Chemla
Maddie Cusimano
Felix Effenberger
Masato Hagiwara
Benjamin Hoffman
Sara Keen
Diane Kim
Jane K. Lawton
Jen-Yu Liu
Aza Raskin
Olivier Pietquin
Matthieu Geist
Venue: ICLR 2026 Poster (2026)
Keywords: bioacoustics
evaluation
benchmarks
audio
sound
classification
detection
clustering
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A comprehensive cross-taxa evaluation of general-purpose audio encoders (19 models) on bioacoustics benchmarks (26 datasets)

Abstract: Bioacoustics, the study of sounds produced by living organisms, plays a vital role in conservation, biodiversity monitoring, and behavioral studies. Many tasks in this field, such as species, individual, and behavior classification and detection, are well-suited to machine learning. However, they often suffer from limited annotated data, highlighting the need for a general-purpose bioacoustic encoder capable of extracting useful representations for diverse downstream tasks. Such encoders have been proposed before, but are often limited in scope due to a focus on a narrow range of species (typically birds), and a reliance on a single model architecture or training paradigm. Moreover, they are usually evaluated on a small set of tasks and datasets. In this work, we present a large-scale empirical study that covers aspects of bioacoustics that are relevant to research but have previously been scarcely considered: training data diversity and scale, model architectures and training recipes, and the breadth of evaluation tasks and datasets. We obtain encoders that are state-of-the-art on the existing and newly proposed benchmarks. We also identify *what matters* for training these encoders, such that this work can be extended when more data are available or better architectures are proposed. Specifically, across 26 datasets with tasks including species classification, detection, individual ID, and vocal repertoire discovery, we find that self-supervised pre-training followed by supervised post-training on a mixed bioacoustics + general-audio corpus yields the strongest in- and out-of-distribution performance. We show the importance of data diversity in both stages. To support ongoing research and applications, we release the model checkpoints as well as the Animal Vocalization Encoder library [AVEX](https://projects.earthspecies.org/avex/) (an API for model loading and inference, and a Python-based system for training and evaluating bioacoustics representation learning models)

single model Published as a conference paper at ICLR 2026
AVEX: WHAT MATTERS FOR ANIMAL VOCALIZATION ENCODING
Marius Miron Earth Species Project
David Robinson T* Earth Species Project
Milad Alizadeh Earth Species Project
Ellen Gilsenan- MMreennn-McMMaoo'' Earth Species Project
Gagan Narula
Emmanuel Chemla Earth Species Project
Earth Species Project
Maddie Cusimano Earth Species Project
Felix Effenberger Earth Species Project
Masato Hagiwara Earth Species Project
Benjamin Hoffman Earth Species Project
Sara Keen Earth Species Project
Diane Kim Earth Species Project
Jane Lawton Earth Species Project
Jen-Yu Liu Earth Species Project
Aza Raskin Earth Species Project
Olivier Pietquin Earth Species Project
Matthieu Geist Earth Species Project
ABSTRACT
Bioacoustics, the study of sounds produced by living organisms, plays a vital role in conservation, biodiversity monitoring, and behavioral studies. Many tasks in this field, such as species, individual, and behavior classification and detection, are well-suited to machine learning. However, they often suffer from limited annotated data, highlighting the need for a general-purpose bioacoustic encoder capable of extracting useful representations for diverse downstream tasks. Such encoders have been proposed before, but are often limited in scope due to a focus a narrow range of species (typically birds), and reliance architecture or training paradigm. Moreover, they are usually evaluated on a small set of tasks and datasets. this work, we present a large-scale empirical study that covers aspects of bioacoustics that are relevant to research but have previ- ously been scarcely considered: training data diversity and scale, model archi- tectures and training recipes, and the breadth of evaluation tasks and datasets. We obtain encoders that are state-of-the-ar on the existing and newly proposed benchmarks. We also identify whal malters for training these encoders, such that this work can extended when more data are available or better architectures are proposed. Specifically, across 26 datasets with tasks including species clas- sification, detection, individual ID, and vocal repertoire discovery, we find that self- self-supervised pre-training followed by supervised post-training coustics general- corpus yields the strongest in- and outtof-distributtin performance. We show the importance of data diversity in both stages. To sup- port ongoing research and applications, we release the model checkpoints as well the Animal Vocalization Encoder library AVEX (an API for model loading and inference, and a wethon-baseer system for training and evaluating bioacoustics representation learning models]
mixed bioa-
Coore authors.
"qqual contribution, corresponding authors: (marius, daviid/Beaathaseediss.crr Equal senior contribution.

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Training Data
+
AudioSet
Xono-Canto
INatura ist, Workins,
Models
Evallation
BirdSet
Individust
Transformer
CNN
Pre-Training
Post-Trainimg
Vocal Repartoire
BEANS
Self Superised Leorning
Supervised Leariing
Figure 1:
Our empirical study diagram, assessing (1) models, (2) training data, (3) training paradigms, and proposing an (4) extended evaluation data and methodology.
INTRODUCTION
Bioacoustics is the study of animal sound production and perception (Bradbury & Vehrencamp 1998). It is a crucial component for understanding animal behavior (Fischer et al. (013), for biodiversity monitoring and conservation efforts (Rutz et al.] 2023 Stevens et al.] 2024), and for modeling the mechanisms underlying animal communication (Bradbury & Vehrencampl 1998). A variety of common tasks in bioacoustics are used to support these efforts: sound event detection or classification of species, individuals, call-types, and behaviors. All these tasks are well-suited for machine learning approach. Machine learning and deep learning are now commonly used for bioacoustics (Stowell) 2022). ) and have enabled discoveries such as the use of specialized vocalizations for labeling conspecifies in marmosets (Oren et al. 2024) elephants (Pardo et al., 2024). However, due to unavoidable challenges in data collection and annotation. these studies generally rely small datasets strongly labeled few species and individuals (Stowell) 2022). The resulting bioacoustic machine learning models are then usually designed for specific tasks and species (Dufourg al. 2021 Cauzinille al. 2024). limiting their generalizabiliy.
However, large amounts of unannotated or weakly labeled bioacoustic data are recorded regularly, especially through Passive Acoustic Monitoring (PAM) (Gibb et al.) 2019), and citizen science plat- forms such as Ceno-Canto (Vellinga & Planqué 2015) or iNaturalist (Chasmai et al. 2024). These data can be leveraged train bioacoustic encoder. which can then be deployed downstream tasks, as bioacoustic features (e.g. for linear probing or clustering) or finetuning the whole model, among other options. Two such classic and state-of-the-aar bioacoustic encoders are BirdNet (Kahl) et al. 2021) and Perch (Ghani et al.) (023) which have been applied to downstream application
tasks such as multi-taxa species retrieval and detection (Pérez-Granados 2023 Dumoulin et al., 2025
Ghani et al. 2023).
Other bioacoustic encoders have been proposed, to be reviewed in the next section. They have in common supervised learning approach, usually limited to single taxonomic group, with notable exceptions including SuriPerch (Williams et al. (024) and recently the models of iNatSounds (Chasmai et al. 2025). Moreover, they evaluate the quality of the learned representations ited set of downstream tasks and datasets. Typically they solely evaluate on species classification, with their training and test data containing the same species, often with out-of-distribution effect, as training datasets typically consists of focal recordings, while evaluation datasets are soundscape recordings (Rauch al., 20256). In contrast, real-world bioacoustic applications require encoders that generalize effectively across diverse species and tasks, often beyond those explicitly seen during training. For example, researchers may need to recognize previously unobserved species, identify individual animals from limited vocal data, characterize animal vocal repertoires without extensive annotations. Evaluating models on such diverse and realistic scenarios is critical, yet building and measuring the performance of encoders that generalize across these conditions remains underexplored in current works.
lim-
Our main contribution is an empirical study assessing what components matter most for training generalizable bioacoustic encoder. We systematically investigate (1) model architectures. (2) data- mixes, and (3) training paradigms under (4) broadened evaluation methodology. (1) Specifically, in terms models we compare CNN-based (LeCun et al.) 1989) and transformer based (Vaswani et al. 2017) architectures alongside their associated learning approaches: supervised and self-supervised.
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(2) On the data mix aspect, we train and evaluate across broader and more taxonomically di- verse bioacoustic dataset than previous work, examining the impact of incorporating general audio data such as AudioSet (Gemmeke et al. 2017). (3) Additionally,
explore sequential training paradigms ("training recipes"), pre-training and post-training, including self-supervised and super- vised learning, and assess the influence of non-bioacoustic audio data different training stages. (4) By evaluating these models across established benchmarks BEANS (Hagiwara et al., (202)) and BirdSet (Rauch et al. 2025b) alongside newly curated datasets assessing generalization chal- lenging real-world tasks, we provide a clearer picture of the conditions that enhance bioacoustic representation learning. We find that under comparable training conditions, self-supervised mod- els achieve strong out-of-distribution generalization yet under-perform supervised models on in- distribution tasks, and that incorporating general audio into bioacoustic training significantly im- proves model transferability. Sequential self-supervised and supervised learning yields strong per- formance both in and out-of-distribution. Leveraging these insights, we propose a set of training recipes and models that achieve offe--of-the-rt results overall on our extensive evaluation bench- mark, offering a versatile encoder for bioacoustic research.
2 RELATED WORK
Self-Supervised Audio Encoders. An extensive number of works propose audio and speech encoders, most of them transformer-baseee such as Wav2vec (Baevski et al. 2020), HuBERT (Hsu al. 202I), AudioMAE (Huang et al., 2022). BEATs (Chen et al. 2023 or EAT (Chen et al., 2024 Several bioacoustic-speccfic encoders have also been developed: BirdNet (Kahl et al. 2021
and Perch (Ghani et al., (023) build upon an EfficientNer (EffNet) architecture (Tan & Le| 2021).
CNN-based vision neural network pre-trained on ImageNet (Russakovsky al. 2015) taking audio spectrograms as input. Transformer-basee
bioacoustic models include AVES (Hagiwara) 2023)
based HuBERT, Vnimal2 (?) based data2vee (Baevski et al.) (023), BirdMAE (Rauch et al. 2025a based on AudioMAE with modified decoder architecture, and TweetyBert (Vengrovski al.
2025) inspired by BERT (Devlin et al., (019). However, within bioacoustics these approaches lack systematic comparison across different architectures with standardized pipelines. In contrast, we compare with wide range of encoder baselines and aim to have fair comparison across different architectures, with minimal changes and near-identical pipelines.
Text-Informed Audio Models and Their Relation to Bioacoustic Encoders. Audio encoders are key in training text-informed models for bioacoustics. BioLingual (Robinson et al. (2024) learns a common representation for text and bioacousties, inspired by CLAP-LAION (Wu et al. 2023)
It allows to perform tasks like zero-shot species classification or text-to-audio retrieval, while bioacoustic encoder requires further learning (like linear probing). NatureLM-audie (rob) (025) is a large audio-language model for bioacoustics, adding audio as input to a Llama 3 model (Grattationi et al. 2024) through a BEAT's audio encoder and a Q-former (Li et al. 2023). In this work, we focus on bioacoustic encoders, and this line of work is complementary to text-informed language models, in the sense these models could benefit from a better encoder. It can also be considered as "post-training" stage for any bioacoustic encoder, that can then be extracted to be used in downstream bioacoustic tasks, for example doing linear probing. As a baseline, we consider extracting the BEATs encoder of NatureLM-audio,, which was unfrozen during training on large- scale bioacoustic data.
large
Data mixing in bioacoustics training. In terms of data composition, existing bioacoustic encoders typically use limited data sources: BirdNet and Perch are post-trained on bird data mostly from Keno-Canto, while Williams et al. (2024) extended Perch to Surfperch by adding coral reef bioa- coustic data. Animal2Ves Vec focuses specifically on meerkats data, and TweetyBert on canary song. General audio encoders are evaluated in extensive audio (Turian al. 2022) and speech benchmarks (Yang et al. (021)) but contain little bioacoustic data in their training mix. While these general- purpose encoders can be used for bioacoustic tasks, /aarkrr & Doss (2025) found that pre-training on bioacoustic data provides only marginal improvements, though other studies reach different con- clusions (Ghani et al. 2023 Rauch et al., 2025a). Our work differs by considering larger and more taxonomically diverse bioacoustic training data, examining the impact of adding general audio data alongside bioacoustic data, and evaluating the impact of data mix under fair settings rather than only evaluating pre-existing models.
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Training Paradigms: Self-Supervised, Supervised, and Two-Stage Approaches. Regarding the training paradigm, current bioacoustic modeling approaches use either self-supervised learning (AVES, Animal2Vec, BirdMAE, TweetyBert) or supervised learning (BirdNet, Perch, Surfperch) exclusively. However, no existing work systematically explores the combination of both paradigms examines the impact of including bioacoustic data at different training stages. We address this gap by considering the combination of both self-supervised and supervised learning. To that extent, our pre- and post-training formalization may be seen as form of curriculum learning (Bengio al., 2009) with two stages, similar to the iterative training of BEATs and commonly used in training LLMs robb 2025).
Evaluation Limitations in Bioacoustic Benchmarks. Bioacoustic encoders have been evaluated primarily in the context of species classification and detection (Rauch et al.) 2025b Hamer et al. 2023 Ghani et al. 2023 Chasmai et al. 2025 Kather et al. 2025) while other important tasks such et al. 2019) have been scarcely addressed. These tasks, which are critical to the study of animal communication yet lack large-scale annotated data, are a natural test-bed for generalization of learned represen- tations. Research on these two topics has so far used limited number of private datasets has not compared with state-of-the-arr bioacoustic encoders (Best et al., [2033] Nolasco et al., [2025]
as vocal repertoire discovery(Anikin et al.J 2018)) or individual identifiiation
Stowell al. 2019 Wierucka et al., 2025). To give broader overview of the capabilities of bioacoustic encoder, we address these limitations by adding 8 public datasets, not considered in any previous benchmark. Further, related work from Kather al. (2025) and from [Best al. (2023)
has gained insight into bioacoustic encoders by analyzing their embeddings with clustering metrics, including qualitative finding that self-super encoders better generalized from birds to frogs. We introduce a similar evaluation methodology, enhancing BEANS (Hagiwara et al. (023) and BirdSet (Rauch et al., 20256) with clustering and retrieval metrics, scaling related analysis from two datasets to twenty-six. For comparison with current bioacoustics benchmarks, we present Table 4 Appx A, which includes training and evaluation configurations.
METHODS
This section provides the details of our empirical study which we summarize in Figure]
3.1 TRAINING DATA
State-of-the art bioacoustic encoders are either trained in a self-supervised manner on large datasets
comprising general audio (Hagiwara (203)) and birds (Rauch et al.) [025a), or in a supervised man- ner to predict the species in focal recordings of birds (Rauch et al. 2025b Hamer et al., [023]] Van Merriënboer et al. 2024 Rauch et al., 2025a: Ghani et al.) 2023). We extend these paradigms by comparing the self superviud and supervised learning on both types of data, general audio and bioacoustic data, both comprising labels. The data are used for two approaches, self-super learning (in which case the labels are ignored) and supervised learning. The general audio dataset is AudioSet (Gemmeke et al. (017) comprising labels for sound event detection within the AudioSet ontology. With respect bioacousties data, we compile large dataset from multiple sources, in- cluding Xeno-canto (Vellinga& & | the largest source of bird signals, iNaturalist (Chas- mai et al. 2024), Animal Sound Archive (Museum für Naturkunde Berlin), which includes diverse taxa, and the Watkins Marine Mammal database "all cuts" (Sayigh et al., 2016) offering the most diverse collection of marine mammal signals. Outside of our core training mix, we consider addi- tional bioacoustic soundscape datasets to study their effect on the learned representations, in par- ticular WABAD Svere--Granadon et al.) 2025) and Sapsucker Woods (Kahl et al.) 2022a). To join diverse bioacoustic datasets, we curate species' scientific names and link all species common taxonomic backbone (GBIF) (Telenius] 2011). We summarize the training data in Table
We train models with noise augmentation (see Section
3.3) using non-animal environmental sounds et al. 2016), Deepship (Irfan et al. 2021
from the following datasets: ShipsEar
and Orcalab (Poupard et al.) (2020). FSD50K (Fonseca et al. Urbansound (Salamon & Jacoby
2014), TUT2016 (Mesaros et al. 2016b), IDMT (Abeßer et al. 2021). Demand (Thiemann et al.
2013 and Wham (Wichern al.
2019).
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Table 1: Datasets used in pre-training and post-training.
denotes datasets used solely in ablations.
Datasct
Hours
Description
Audioset Gemmmeee ct al.
2017
5700
general audio
Xeno-cante (Vellinga & Planque 2015
10416
birds
INaturalist. CChasmar
2024
1539
diverse taxa
Watkins (Sayigh 2016
marine mammals
Animal Scund Archive (Msseum für Naturkunde Berlin
diverse taxa
Sapsucker Woods" (Kahl et al 2022a)
285
birds
WABAD"
et al 2025
84
birds
3.2 PRE-EXISTING ENCODERS
We consider various pre-existing encoders, as baselines and for further benchmarking and analysis. First, we consider general audio encoders,
specifically BEATs (Chen et al. (023) and EAT (Chen et al. 2024), BEATs because it a state-of-the-ee encoder, and EAT because we will modify its self-supervised training recipe; Chen et al. (2023) do not provide the training code, only trained checkpoints, and EAT is a good and (fully) open-sourced model.
We also include bioacoustic encoders as baselines, namely BirdNet (Kahl al.) 2021 and Perch (Ghani et al. 2023) as state-of-the-ar baselines. In addition, we evaluate SurfPerch (Williams et al., 2024) because it uses more diverse taxa in training. We also consider AVES (Hagiwaral 2023) and
BircMAERauch et al. 2025a) representative self-supervised models for bioacoustics.
For the last baseline, we extract the BEATs encoder from NatureL (rob 2025). that we call NatureBEATs, as representative of an unorthodox post-trained encoder. Comparing it to BEATs can provide clues about the influence of text-audio training and of post-training with bioacoustic data, in addition to the experiments to be described now.
3.3 PROPOSED MODELS AND TRAINING RECIPES
We provide summary of the models we train according to data used in pre- and post-training in Table[] As explained before, both BirdNet and Perch build upon an EffNet post-trained on (mostly) Xeno-Canto, using a multi-label supervised learning loss. To mimic this approach, we also con- sider an EffNet architecture, with checkpoint pre-trained on ImageNet, which we post-train with supervised learning and a binary cross-entropy loss. To assess the utility of using bioacoustic data, possibly complemented by general audio data, we consider post-training this model on bioacous- tic data only, on AudioSet only, on both. The version post-trained solely bioacoustic data is reminiscent of BirdNet or Perch, even if it is not an apple-to-apple comparison (each model uses different bioacoustic dataset, normalization method, sampling rate, spectrogram parameters, and augmentation details). We take advantage of the efficiency of the architecture to test various data- mixes, including the addition of soundscape data, and the ablation of large taxonomic subgroups such as birds, whales, or all nor-birds. The results for this ablation are presented in Figures and in the Appendix.
Our approach does not rely specifically on the EffNet architecture. Therefore, we propose to do the post-training of another, transformer-based, audio encoder. We chose BEATs, it is state- of-the-an art encoder, pre-trained on speech and general audio data with self-supervised approach. We post-train it in a supervised manner on our bioacoustic data only, or on both bioacoustic data and AudioSet. We exclude post-training BEATs on AudioSet only as this corresponds to the public checkpoint BEATs (SFT).
We can also envision modifying the self- pre-training phase. Unfortunately, the training code for BEATs not available and not straightforward to reproduce. Therefore, we turn to the EAT model (Chen et al. 2024), which also provides good results, is fully open-source, and has the advantage of being fast to train. We consider pre- ve-trrannng EAT without modifying its self-supervised learning approach (which is mix of teacher distillation and reconstruction of masked patches of the spectrogram), on our bioacoustic data only, on AudioSet only, and on both. Then, we consider not post-training this model (to assess if post-training is useful, especially given that it is the same
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dataset, up ignoring or not the labels), and also post-training it before (bioacoustic data only, or bioacoustic data plus AudioSet). Similar to BEATs, we do not post-train EAT on AudioSet only as this matches existing checkpoint, "EAT (SFT)". We use the "EAT-all" SSL checkpoint as the basis for post-training.
For fair comparison with pre-existing models train and evaluate all models 16kHz, whilst we acknowledge that some species contain important auditory information above 8kHz and we plan extend this study in future work. We evaluate Perch and BirdNet their proposed sample rates 32kHz and 48 kHz, and to their advantage, they observe a broader frequency spectrogram than our base EffNet.
To increase generalization and robustness to noise of the learned representation we found it impor- tant to use two augmentations. Namely, during pre-training and post-training we add noise randomly with probability of 0.5 at a random signal-to-ooise ratio (SNR) sampled from uniform distribu- tion between 1OOBB and 20dB using the datasets introduced in Section [31] During post-training, with probability 0.5 we linearly mix random pairs of audio clips within a batch and set the target to the union of their labels (element-wise OR).
Table 2: Pre- and post-training datasets with resulting model checkpoints. t Indicates checkpoints released by prior work. "Adi=ee All post-trained EAT models use the FAT-all checkpoint as the base. Arch.
Pre-train data Pre-trained checkpoint
Post-train data
Resulting checkpoint(s)
EffNetBO ImageNet!
Bio/ A All/ AS
EffNetBO-bio,
EffNetBO-AS
BEATs
ASI
BEATs (pre)*
Bio All/ All/AS
sl-BEATs-bio sI-BEATs-all, BEATs (SFT)*
EAT
Ast
EAT-base (pre)*
AS
BAT-base (SFT)*
EAT
Bio / All/ AS
EiT-bio, EAT-all, EAT-AS
Bio / All
sl-EAT-bio, sl-EAT-all
3.4 EVALUATION SETUP: DATA, TASKS, AND METRICS
Evaluation Tasks. We consider two tasks commonly addressed in the literature: classification on audio excerpts into discrete category labels and detection of events in longer audio files. We employ three distinct evaluation setups to assess model representations comprehensively. Linear probing trains a linear classifier on train split using time-averaged embeddings from the final layer of the model (excluding classification heads), and then evaluates this probe on the test split. Retrieval evaluation directly investigates model embedding spaces by treating each test set item as a query and ranking remaining items by cosine similarity. Clustering evaluation performs K-means with known cluster counts on single-label datasets, evaluating similarity to ground truth classes using normalized mutual information (marked as NMT)
Probing, Retrieval, and Clustering. Current bioacoustics benchmarks consider either an in-domain multiple taxa setup, such BEANS (Hagiwara et al.) 2023) and (Robinson et al. 2024), or out-of-domain bird species detection, such as BIRB (Hamer et al.) 2023) and BirdSet (Rauch et al. 2025b), focusing on generalization. Here we extend these benchmarks in multiple ways. (1) We expand the tasks to include individual identification and vocal repertoire discovery, along with the curation of eight additional benchmark datasets. This extends the evaluation of bioacoustic encoders from focus on species classification of birds to focus on generality across multiple tasks and taxa. (2) Extending the work of Kather al. (2025), we also augment existing benchmarks with clustering and retrieval metrics, allowing us to examine model's s embedding spaces directly. Each of these benchmarks, tasks, and metrics is directly tied to downstream research or conservation applications and allows us analyze both pre-existing and new models from a different perspective.
Evaluation Paradigms:
Evaluation datasets. We present the datasets and tasks consider as part of our evaluation in Table 4. Appendix A We evaluate on the classification and detection tasks in the already existing benchmarks BEANS (Hagiwara al. 2023) and BirdSet (Rauch al. 2025b). For BEANS

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follow the official train/validation/tes splits in the original benchmark. We exclude the auxiliary dataset SpeechCommands from BEANS but we include ESC-50 because it still may be useful for conservation tasks relevant to bioacoustics e.g. habitat classification, poaching monitoring. For BirdSet, we match their "Dedicated Train" setup, considering separate train and test splits for each of the datasets. Accordingly, train with 0.8, 0.2 ratios and seed 42. We exclude SSW (Kahl et al. (202b) from BirdSet evaluation as we consider it within some of our data-mix. We formulate detection similarly to BEANS and BirdSet as segment- based multi- class classification and we use segment- segment-based sound event detection metries to evaluate it (Mesaros et al.. 2016a), allowing for the negative class (no class detected). We leave frame-based (Mesaros et al.] (0T6a) or event-based (Mahon et al., (025)) temporally-strone detection for future work. Importantly, solely for BirdSet which contains an important covariate shift
derive the validation dataset stratified split on the species from the
from train (focal recordings) to test (soundscapes) we use the same noise and mixup augmentations in pre- vre-training and post post-training.
Individual Identification and Vocal Repertoire Evaluation. For the two new evaluation tasks we compile public datasets and create label-stratified train//aalddation/eest splits (seed 42, ratios 0.6/ 206.2/0..2), The Individual Identification task (Stowell et al. 2019 Fukushima et al., 2013) is a supervised single-label classification problem over individuals of the same species. The Vocal Repertoire Discovery setting (Elie & Theunissen 2016) Mumm & Knörnschild] 2014 Cohen et al. [2020] Palmer et al. 2025) evaluates how well embeddings discrim- inate between the different call types within species' vocal repertoire. We treat this as a structure- recovery problem with known A (the number of annotated call types): no probes are trained; labels are used only as a reference assess representation quality via (i) clustering (K- (K-means with K equal to the number of call types; scored by normalized mutual information, NMI) and (ii) audio-to-aus -udio retrieval within call type (ROC AUC, RUAC). This matches repertoire discovery when K is known. Supervised call-type classification is also useful formulation of the task, but performance eral datasets was near-ceiling while others were too small for quality splits, so we exclude linear probing for this task.
New Evaluation Datasets:
sev-
Probing Evaluation Setup To directly evaluate learned representations, we use linear probing on time-averaged embeddings rather than full fine-tuning as Hagiwara (2023); Rauch et al. (022b)). This avoids confounding effects from model size differences, ensuring fair comparison between CNN and transformer representations. While performance may vary across embedding layers, all models show similar trends (Cauzinille et al., [024] [Sarkar & Doss] (022).. Evaluating all layers would exponentially increase computational cost, we extract embeddings from the final layer (excluding classification heads) and leave comprehensive layer analysis for future work. Considering a frozen representation (rather than fully finetuning) also especially important for many downstream applications, as it allows notably precomputing the embeddings (Dumoulin et al.) 2025). In terms of hyperparameters we use a learning rate of le-4, weight decay of 0.1, batch size of 32, and 900 epochs.
Metrics. For all tasks other than Vocal Repertoire Discovery we report classic performance metrics on linear probing (top-1 accuracy for single-label tasks, macro-averaged mean average precision for multilabel tasks) To evaluate retrieval, we consider each item the test set as a query. We rank the remaining elements of the test set according to their cosine similarity with the query under the model's s embedding function, and evaluate the ordering. For single-label tasks (BEANS Classifica- tion, Individual ID, and Vocal Repertoire), the same label as the query. For multi-label tasks (BEANS Detection, BirdSet) we consider items to be relevant if they share at least one label with the ground-truth item. We exclude queries with no labels, while ground-truth items with no labels are included in the evaluation as negatives. We mea- sure this ranking using ROC AUC the primary metric (marked as R-AUC with R for retrieval). For Individual Identification evaluation datasets Pipit and Chiffchaff (Stowell et al. 2019), the train- ing set follows an intentionally different background noise distribution than the test set following original design in Stowell et al. (2019). To evaluate generalization in this scenario, we report ROC AUC by using each example in the train set as a query, rather than each example in the test set as a query. For single-label datasets in all benchmarks, we also consider clustering task. For each eval- uation set, we perform K-means clustering given a known number of clusters (labels). We evaluate similarity of these clusters compared to the known class groups by measuring normalized mutual information (marked as NMD) between the two. These two additional evaluations offer insight into the pre- pre-trained embeddings of model, but are also relevant to downstream tasks in bioacoustics,
consider items relevant to the query if they share
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namely audio-to- aodioooo-audio retrieval (Hamer al. 2023) (retrieval) and repertoire discovery (Best al. 2023 (clustering) or call-type classification. For vocal repertoire discovery, we use clustering and retrieval as the primary evaluations. All metrics are formalized in the Appendix in B.2
RESULTS
4.1 WHAT DATA MATTERS IN POST-TRAINING EFFICIENTNET
We present results for our EffNet models aggregated across benchmarks in Table 3 the shaded rows, We compare to Perch, SurfPerch, and BirdNet - all EffNet-based and considered state-of-the- art bioacoustic encoders. The results highlight the value of our diverse curated bioacoustic datasets, with our best EffNet model outperforming on eight of ten metrics. We observe a consistent per- formance gain of including general audio in the data-mix, transferring across focal classification, multi-label classification on soundscapes, vocal repertoire, and individual ID tasks. Consistent with other works (Ghani et al. 2023), supervised training on general audio alone transfers poorly com- pared to bioacoustic data. Interestingly, this holds for the newly benchmarked Vocal Repertoire and Individual ID tasks, suggesting large-scale species-predife prediction an effective approach for transfer to these tasks, which are often studied independently. We further characterize what supervised data transfers which tasks and species through further ablations in the Appendix in Figure
4.2 SELF-SUPERVISED PRE-TRAINING HELPS OUT-OF- DUS-OI-DISTRIBUUION
Comparing the results of EAT models trained with self-supervised learning, we find strong effect of including general audio in the data-mix, with the model trained with the addition of AudioSet significantly outperforming the bioacoustics-only model across tasks (Figure2u..
We further compare our supervised and sule--uue models (Figure 2b) alongside existing mod- els, and alongside post-traine post-trained SSL models, discussed in the following section. For this comparison we want to see how discriminative raw representations from the models are with respect to the tar- get datasets, and we use ROC AUC as a training-free metric. Compared across benchmarks, this analysis should give an idea about how generalizable the embedding space is. In this analysis we trade-off experimental control for scale, using large datasets. Running the same analysis on datasets where we control for species distribution, noise conditions, and other confounders should give better explanation on how robust these representations are. Because we are not aware of any dataset that offers this controlled conditions we leave this for future work.
While supervised models excel in tasks closely matching their training distribution, self-supervised models demonstrate superior generalization capabilities on out-t--distiibutiinn tasks. Specifically, we find when generalizing from BEANS classification (typically focal recordings) to BEANS De- tection (entirely soundscape recordings) the self-supervised models drop on average only 0.01 re- trieval ROC AUC compared to a drop of 0.09 retrieval ROC AUC for the supervised models. The strength of the effect sufficient that the best pure self-supervised model, the pre-trained BEATs, outperforms the strongest pure supervised models by retrieval on BEANS Detection. This finding underscores the strong potential of self-supervised learning in bioacoustics, where supervised lear- ing is still considered state-of-the-art, yet models are challenged by huge distribution shifts between training and deployment (Hamer al. 2023).
4.3 POST-TRAINING RECIPES FOR SULE-VUPERIS BACKBONES
We show the results of self-supervised backbones post-trained on our bioacoustic dataset, compared to Mateeof-thhe-ar bioacoustic encoders in Table Our best post-trained models outperform overall, achieving state-of-the- art performance across the established BEANS Classification, BEANS Detec- tion, and BirdSet benchmarks, outperforming both their self-supervised base models and supervised baselines. On the marks, the post-trained models maintain competitive performance, but the newly-trained EffNet on mixed bioacoustic and general audio data performs best, and BirdNet performs strongest by retrieval on Individual Identification. With respect to the the data-mix, the BEAT' model trained on the mix of general audio and bioacoustic audio outperforms overall, while the mixed training has a more vari- able effect EAT. We additionally find that post-training retains some of the -ittr--distribttion
Vocal Repertoire Discovery and Individual Identification bench-
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BEANS Classification R-auc
BEANS Delection
BirdSet
Individual
Vocal Repertoire
Model
Probe
C-nmi
Probe
R-auc
Probe
R-avc
Probe
R-auc
R-auc
C-nmi
BEATS (SFT)
0.724 0.739
0.504
0.339
0.692
0.101
0.675
0.375
0.602
0.755
0,485
BEATS (pretrained
0.774
0.734
0.542
0.381
0.722
0.129
0.686
0.380
0.637
0.775
0.498
EAT-base (pretrained) SSL
0.679
0.675
0.424
0.252
0.692
0.104
0.650
0.363
0.623
0.768
0,467
EAT-base (SFTy"-s9
0.758
0.748
0,478
0.358
0.714
0.143
0.676
0,418
0.632
0.817
0.527
Bird-AVES-bio--base
0.705 0.646
0.410
0,340
0.689
0.092
0.670
0.402
0.622
0.726
0.453
NatureBEATs M-S81.
0.804 0.774
0.560
0.385
0.724
0.223
0.723
0,410
0.645
0.811
0.552
Bird-MAE-Huge SSI
0.766 0.674
0.432
0.354
0.680
0.168
0.636
0.404
0.637
0.812
0.485
SurfPerch St
0.760 0.745
0.484
0.301
0.664
0.160
0.694
0.457
0.656
0.751
0.492
SirdNet
0.796 0.772
0.523
0.392
0.687
N/A
N/A
0,472
0.708
0.795
0.545
Perch SL
0.768 0.759
0.478
0.368
0.674
0.233
0.656
0.530
0.705
0.758
0.493
ERNNetB-AuudoSee"
0.651 0.721
0.486
0.246
0.670
0.098
0.655
0.397
0.612
0.760
0.481
EHfNetBO-bio SL
0.786 0.799
0.563
0.365
0.695
0.279
0.704
0.457
0.683
0.806
0.568
E
0.800
0,809
0,584
0,362
0.712
0.279
0.707
0,531
0.701
0.830
0,582
EAT-ASSSL
0.704
0.714
0.473
0.311
0.704
0,125
0.685
0.362
0.627
0.801
0.533
EAT-bio
0.692 0.671
0.410
0.311
0.679
0.143
0.631
0.378
0.627
0.757
0.466
SAT-allst.
0,709 0.704
0,448
0,315
0.694
0.166
0,677
0,348
0.611
0.788
0,512
sl-BEATS-bio SL-SSL
0.840
0.811
0.594
0.390
0.719
0.288
0.726
0.484
0.681
0.789
0.516
s-BEATS-alI StA...
0.832
0.813
0.604
0.408
0.726
0.294
0.732
0,511
0.690
0.798
0.529
st-EAT-bio M-SS.
0.797
0.792
0,562
0,353
0.687
0.249
0,705
0,495
0,672
0.806
0,565
sl-EAT-allSt-St.
0.788
0.791
0.536
0.356
0.704
0.255
0.706
0.456
0.637
0.798
0.530
Table 3: Aggregate results across bioacoustic benchmarks and tasks (best per metric in bold). We report ROC AUC for retrieval, accuracy for probing on BEANS classification and Individual ID, mean-average precision for probe on BEANS Detection and BirdSet. We report the mean of each metric over datasets per benchmark. BBrddet results on BirdSet are excluded following the authors (Rauch et al. 2025b) due to data IcakageModel labels carry training tags: SSL self-supervised, SL supervised, supervised fine-tuning after SSL pretraining. Models above the midrule are exist- ing/pretrained checkpoints; below are new models from this work. EfficientNet models are shaded.
gains of the pre-trained backbone, yielding models which are strong both in and out of distribution, maintaining the benefits of both paradigms- we visualize this in Figure 2b. While post-training both EAT and BEATs gave consistent improvements vs. their raw SSL models (Figure in the Appendix), solely the post-trained BEATs achieved SOTA results overall, possibly suggesting that stronger SSL backbones may lead to better post-trained models. The other existing bioacoustic post- trained self- supervised backbone NatureBEATs follows closely several benchmarks, significantly outperforming pre-trained BEATs and outperforming supervised baselines on multiple benchmarks. Interestingly, we observe the pre-trained NatureBEATss extends the line of the self-supervised mod- els, while our post- trainer models behave more like stronger supervised models (Figure (b.) This discovery provides interesting signal for future work on post-training under different paradigms. Overall, these results strongly support our proposed recipe of self-supervised training on diverse data-mixes of bioacoustics and general audio, followed by supervised post-training on the same mix. They also show supervised and self-supervised learning in bioacoustics are complementary for representation learning, and suggest simple step to improve overall quality of self-supervieed bioacoustic encoders, not yet commonly adopted (Hagiwaral [023 Rauch et al. 2025a). We share full, non aggregage results for all models in the Appendix in Tables 6
and
5 CONCLUSION
We presented the first large-scale empirical study and recipe for developing generalizable bioa- coustic encoder. With few architectural assumptions, we believe this recipe can scale as both labeled bioacoustic data continue to grow and self supervieed learning continues to improve.
We benchmarked 19 CNN- and Transformer-basee models across 26 datasets and four task families. We demonstrated that self-supervised pre-training mixture of broad bioacoustic and general- audio data, followed by supervised post-training on the same mix, yields the best in- and of distribution results, outperforming state-of-the-ar baselines such as BirdNET, Perch and BEATs on
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Benefit of mixing general audio in pretraining
(a)
100. 0%
(b)
Bloacoustic, generalization separates
85.7%
Training Approach
BEAts-Natyrel Me--atte---autiiiii M-aul Bh T----
+5.5%
0.72
Existing St.
BEATS (nntriiiee)
sHil 4 S-bii
Batrieunl ARMA Dutection RLAUCC
SSL
EAT
Eritiggo
Existing SSL
Win-Rate (%)
+10.1%
6/10
-6.0%
5/8
Post-treined SSL
si Talll
En
retraine
BEA
&
WES
Birgiv ----o
& Rrgane (rainnedd)
Pagch
20
Sur Barch
3.675
0750
3.755
Figure 2: (a) Win-rate of adding AudioSet self-supervised pre-training vs. pure bioacoustic data, with average relative gain per metric. (b) Supervised encoders outperform self-supervised on BEANS classification, which is primarily focal recordings. However, self-supervised encoders suf- fer markedly smaller performance drops than supervised encoders when moving from focal record- ings to soundscape (BEANS Detection), showing strong outto--distributiin performance. In con- trast, self-supervised encoders post-trained with supervised learning bioacoustic data enjoy the strongest performance both in and out-of distribution.
existing benchmarks (BEANS, BirdSet), and also on newly introduced benchmarks for individual identification and vocal repertoire. Diverse training audio, especially adding AudioSet, consistently improved transfer, whereas supervised training on general audio alone transferred poorly.
Beyond models, we broaden bioacoustic evaluation by curating new benchmarks for individual iden- tification and vocal repertoire classification from public datasets, and by augmenting existing suites with retrieval and clustering metrics. These additions probe representation quality directly and are aligned with practical tasks such audio-to-audio retrieval and repertoire discovery. We observe that scage-scale bioacoustic pre-training is an effective path toward representations that generalize to these under-studied tasks.
Together, these findings provide actionable recipes for building versatile encoders and a richer benchmark for future research. By open-sourcing encoders hope this line of work will be used to accelerate research in animal communication and conservation applications through bioacoustics.
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Joachim Thiemann, Nobutaka Ito, and Emmanuel Vincent. The diverse environments multi-channel acoustic noise database (demand): A database of multichannel environmental noise recordings. Proceedings of Meetings on Acoustics, 19( 193500,,, 05 2013. ISSN 1939-800X. doi: 10.1121/ 1.4799597. URL //tttpp:://d..... /tt.////11... 1121/1 01T11//1.44999999
Joseph Turian, Jordie Shier, Humair Raj Khan, Bhiksha Raj, Björn W Schuller, Christian J Stein- metz, Colin Malloy, George Tzanetakis, Gissel Velarde. Kirk McNally, et al. Hear: Holistic evaluation of audio representations. In NeurlPS 2021 Competitions and Demonstrations Track, pp. 125-145 PMLR, 2022.
Bart Van Merriënboer, Jenny Hamer, Vincent Dumoulin, Eleni Triantafillou, and Tom Denton. Birds, bats and beyond: Evaluating generalization in bioacoustics models. Frontiers in Bird Science, 3: 1369756, 2024.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention all you need. Advances in neural informa- fion processing systems, 30, 2017.
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Willem- Vellinga and Robert Planqué. The xeno-canto collection and its relation to sound recog nition and classification. CLEF (Working Notes). 1391, 2015.
George Vengrovski, Miranda R Hulsey-Vines Vincent, Melissa A Bemrose, and Timothy Gardner, Tweet- ybert: Automated parsing of birdsong through self-supervised machine learning. bioRxiv, pp. 2025-04, 2025.
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Kaja Wierucka, Derek Murphy, Stuart K Watson, Nikola Falk, Claudia Fichtel, Julian León, Stephan T Leu, Peter M Kappeler, Elodie F Briefer, Marta B Manser, et al. Same data, dif- ferent results? machine learning approaches in bioacousties. Methods in Ecology and Evolution, 2025.
Ben Williams, Bart van Merriënboer, Vincent Dumoulin, Jenny Hamer, Eleni Triantafillou, Abram B. Fleishman, Matthew McKown, Jill Munger, Aaron N. Rice, Ashlee Lillis, Clemency E. White, Catherine A. D. Hobbs, Tries B. Razak, Kate E. Jones, and Tom Denton. Leveraging tropical reef, bird and unrelated sounds superior transfer learning in marine bioa- coustics, 2024. Uttptt http://axvvv.oo oww. abs 240..4404... 16436
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A TRAINING AND EVALUATION DATA COMPARISON
To complement the related works discussion in Section we provide comparison with current bioacoustics benchmarks in Table including both training and evaluation configurations.
Table 4: Training and evaluation data comparison for papers comparing audio encoders across mul- tiple species benchmarks. The ones from rob (2025) were reframed for zero-shot learning.
Daloot
2004
2023
Chasi
2023
Williaro
Huser
Pre-traming
WIK
ASA
Phat-traning Uint
AS
XC
WTK
ASA
BEANS Chnciicatin
DOG
CBI
BWTTK
1ST
RFCX
IDC
GIB
DCASS
PER
NBP
[ISN
Individual ID
PIP
Vocal Repertoire
ZFIN
OTT
diberdat
B EXPERIMENTAL SETUP
B.1 EVALUATION METRICS
B.2 PERFORMANCE METRICS
We formalize the evaluation metries we introduce Section 3.4 We evaluate linear probing with accuracy for classification, and macro-averaged mean-average precision for detection. We evaluate retrieval with ROC AUC and clustering with NMI. We formalize all evaluation metrics below.
Linear Probing Performance
la. Top-1 Accuracy (for classification):
N A
1 N
Σ
T(ys = (s)
(1)
1=1
where is the number of samples, y; is the true label, 0; is the predicted label, and the indicator function (Hagiwara 2023 Rauch al. 20255).
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lb. Average Precision (AP). For each class k, let mi be the permutation of {1....,NN N} that sorts examples by decreasing score for class k. Define
P(i) = TP.
4 1 (1) = 1}, TP:
j=1
The (non-interpolated) average precision for class At is
N
APR
P(i)
max
1, L
Vo.k
and the mean average precision is the macro average over classses
mAP K
APR
Retrieval Performance (Area Under the ROC Curve):
S." (PR(FPR) dFPR
AUC
(2)
where TPR
TP-FN and FPR
TP
FP
with TP. FP, TN, FN being true/false posi-
EP+-NN
tives/negattves (Rauch et al. 2025b Hamer et al. 2023).
Clustering Performance (Normalized Mutual Information):
2 I(U,V)
NMI(U, H(U) + H(V)
(3)
where U and are cluster assignments, [UU V) is their mutual information, and (U), HVV) are their entropies (Kather et al. 2025).
B.3 WIN-RATE
We quantify the benefit of post-training self-supervised learning (SSL) backbones through a win-rate analysis that compares post-trained models against their corresponding base models across multiple benchmarks and evaluation metrics, For each post-tran post-training pair (Mpost Muase), where Mpost denotes a post-trained model and Musse corresponding base model, we compute the relative percentage improvement for each metric m and dataset d as:
A Am posts Musee) S., Sm, (pona)) Sm, Smma(Muusee (Muse) x 100%
(4)
Smaa(Mose))
where (MM represents the score of model M on metric for dataset d, defined as: Sm,a Aa(M) for linear probing accuracy on classification tasks, Sm,a(M) = mAP (M) for mean average precision on detection tasks, Sm,d(M)) (M)) NMI for clustering performance. Cases where Sm,((Maas = 0 are excluded from the analysis avoid division by zero.
AUC₂(M) for retrieval performance, and
For each benchmark B, we define the set of valid metric-dataset combinations MB {m, d) m E Metrris (B), E Datasets(B)) {B)), where Metrics () and Datasets(B) denote the metries and datasets associated with benchmark B, respectively. For a given post-training pair (Mpoat Masee and benchmark B, we define binary win indicator Wm.d E {0, 1} nation (m, d) E M::
for each metric-dataset combi-
{
if am Mhuse) >
W m.d
otherwise
The win-rate wp for benchmark and post-training pair (Mposs Mosse) is then computed as the percentage of wins:
WB Mpost Muuse)
Wm.d x 100%
(6)
[MB
(m,d)EMa
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where [Myl denotes the cardinality of MM (i.e., the total number of valid metric-dataset combina- tions for benchmark B). Note that Wm.d. is a binary indicator for individual comparisons, while wi is the aggregated win-rate percentage. To obtain overall assessment of post-training benefits, aggregate win-rates across all post-training pairs P = {M poars M JN where is the number of post-training pairs. The aggregated win-rate for benchmark B is:
En E(m.deeM (
SEE W
m.d
100%
B
ΣN EM
where Wr(t) m.d denotes the win indicator for pair and the number of valid combinations for pair i on benchmark B. average percentage improvement across all comparisons:
combination (m, d), and |M Additionally, we compute the
AB B
Σ
A m,d
(8)
B
(m.d)eMM (
m.d is the improvement for pair i on metric-dataset combination (m, d). Our analysis considers the following post-training pairs: EAT-AS: (sI-EAT-AS, EAT-all), EAT-bio: (sl-EAT-bio, EAT-all), EAT-all: (sl-EAT-all, EAT-all), BEATS-bio: (sl-BEATS--io, BEATS (pretrained)), and BEATS-all: (sl-BEATS-all, BEATS (pretrained)), where the notation "sl-" indicates a model that has been post-trained with supervised learning on downstream tasks.
where A (8)
B.4 DATA SOURCES
2021 version of AudioSet, 2023 versions of Watkins "All cuts" and Animal Sound Archive, June 2023 versions Xeno-canto, iNaturalist. All the training data was released un- der Creative Commons licenses on the respective platforms, with the exception of Watkins for which we received appropriate licensing agreements, including permission to redistribute. We downloaded BirdSet using the Huggingface dataset library For BEANS used the respective scripts the authors provide in their repository
For training
For the benchmarks use the public repositories of Bengalese Finct Zebra Finct Giant Ot- 2026 killer-whale -rriiu-
ters DCLDE 2026 Killer hhhallatts//cctaaoooodddao/oo/to Bird ID Macaques Coo Calls"
brca-ecotype-and-olieee hotp:y/ccaaccooooeee-eeemoooooo-
B.5 SOFTWARE IMPLEMENTATION
Our experimental pipeline is implemented in Python using the Pytorch library. We have used fixed random seed (42) for generating the datasets and as initial seeds for Pytorch and numpy.
We used open-source implementations for: BEAT 10
EAT and EfficientNetBO from torchvi- We wrote pytorch wrappers for BirdNet and Perch using tensorflow-lite.
sior
B.6 HYPERPARAMETERS
We include the full hyperparameters for our trained models in Table
com//eartsspeeeis//eanns
https//figshare
hwwp:////////////aaa//////aa/aee Finch repositor//480057
https//aachhiioooogeelllimmmaaa coo calls
http///cweecc...mmmmmmmmmeeeeeee h0.html
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Table 5: Training hyperparameters for all model variants
Model
Training
Stage-1
Stage-1
Epochs
LR
Batch
Optimizer
Weight
Scheduler
Warmup
Data
Epochs
LR
Size
Decay
Steps
EfficiondNer Variants
EdfNeiBO-AS
AudioSet
NA
NA
50
Se-4
256
AdamW
0.01
Cosine
4000
EilNeiBB-bbi
bio
NA
NA
50
Se-4
256
AdamW
Cosine
4000
EilNeiBO-all
AS Bio
NA
NA
50
Se-4
256
Adamw
Cusine
4000
BffNceBO-coundccup
Bio
NA
50
5o-4
128
AdamW
Cosine
4000
Soundscape
EiiNeiBO nobirds
Bio(no
NA
NA
50
Se
256
AdamW
0.01
Cosine 4000
hirds)
EffNeiBO nowhales*
Bio AS
NA
NA
50
Se-4
256
Adamm
0.01
Cosine
4000
wholes)
EffNeiBO-birds
Bio AS
NA
NA
50
5o-4
256
AdamW
0.01
Cosine
4000
(birds only)
BEAT, Voriants
3--BEATs-all
All datasets
5c-4
10
1c-4
256
AdamW
0.01
Cosine Cosine
5000
<-BBEATs-bio
Bio
5e-4
10
1e-4
256
AdamW
0.01
5000
EAT Variants
BAT-bio
Bio
NA
NA
30
lc-4
48
AdamW
0.01
Cosine
53333
EAT-all
AS Bio
NA
NA
30
Ie-d
48
AdamW
0.01
Cosine
53333
EAT-AS
AudioSet
NA
NA
30
lc-4
48
AdamW
0.01
Cosine
53333
bii
SSL Bio
le-4
10
8e.5
256
AdamW
0.01
Cosine
2000
st.EAT-all
SSL All
le-4
10
8e.5
256
AdamW
0.01
Cosine
2000
Ablation studies with filtered training data.
BEATs and EAT sl. models have initial stage with backbone frozen, with cosine scheduler for stage-1 epochs
with Stage-1 Ir
Bio = core bioacoustic data, AS = AudioSet, SSL = Self- superviue learning.
To select the hyperparameters started from the ones proposed in their original papers. In the of BEATs the learning rate we started from lc-4 which is the original peak learning rate. We did 5k warmup steps, the same the original paper. We found that several works, particularly DCASE challenges are doing the same. For EAT we found it important to decrease the learning rate with respect the original paper (5e-4) because of the larger batch size. For AVES keep the learning rate (Ie-4) which was used in the BEANS benchmark, the test-bed for this model. For probing we use learning rate of le-4, weight decay of 0.1, batch size of 32, and 900 epochs.
C ADDITIONAL RESULTS
C.I BENEFITS OF POST-TRAINING VS. RAW SSL
As shown in Figure 3 post-training SSL encoders with supervised learning provides a consistent improvement vs. raw SSL backbones, sometimes with large relative gains. These results show that supervised learning can have complementary benefits to self-supervised learning for bioacoustic representation learning. They also give clear evidence for those developing self-supervised models to post-train supervised learning, even when the objective is transfer to out-tf-distributiin tasks.
C.2 A BLLATION ON TRANSFER OF TRAINING DATA TO DOWNSTREAM TASKS
We show additional ablations on transfer of various data-mixes to downstream tasks in Figure and Figure 3 From a baseline of (focal) bioacoustic data only, we show the performance of adding general audio, adding soundscape recordings, and ablating different taxonomic groups (whales, and then all taxa but birds.) Adding general audio to the training mix improved results overall, but in particular transferred consistently across our vocal repertoire datasets. This data mix yields large gains the ESC-50 dataset evaluating representations of general audio; though unsurprising, this is a relevant benefit for bioacoustic encoders in tasks such as classifying environmental noise. Training on only general audio data dropped performance very significantly overall, but the drops were most severe on BEANS Classification tasks well-informed by species prediction, and relatively smaller on detection. Adding soundscape data into the training mix with focal is tempting strategy for learning
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Benefit of Post-training SSL Backbones
89. 5%
26/32 +52.5%
80-
70.8% +37.5%
68.8% +4.8%
Win-Rate (%)
+5.3%
HEANS Classiication
BEAKS
Birbbt
Indivdual
Vocal Repertoree
Figure 3: Win-rate of post-trained SSL models their SSL backbones. We plot the win-rates summing over all metrics for all our post-trained (EAT and BEATs) models, and show the average relative gain per model with respect its base model.
Data Mix Ablation (from Bloacoustic
BEANS Benchmarks: Training Mix Effects Across Taxa Tasks
BEANs Classification
BEANs Detection
Whales
Birds Only
6:223
EGB
6:44
AC14
A44a
4:54
4.00
384
6:M
4G0
A043
-0.15
-0.10
-0.05
0.00
5.01
8.10
0:15
Performance Dalta from Baseline (Sioacoustic Only]
Figure 4: Detailed transfer of training data
and tasks in the BEANS benchmark. Heatmap shows the performance change for an EfficientNet trained on each data mix as compared to a base- line "bio" dataset. "_ Bio General" trained on only AudioSet, "+ Soundscape" adds sound- scape datasets, " Whales" ablates all marine mammal recordings, "Birds only" removes all non-bird recordings.
improved representations useful for downstream tasks on soundscapes, used e.g. by later versions of BirdNet (Kahl al. 202T). However, in our ablation, this did not give consistent improvements, possibly due to the lack of diversity in the easily accessible soundscape data.
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Data Mix Ablation ffrom Bioacoustic
BirdSet, Individual ID & Vocal Repertoire: Training Mix Effects
Repertoire
General Audio
ABC
6G3
<:01
640
<:26
LGD
LGC
C.l
C43
Colt
6CB
Bio General
448
4050
4110
6:11
6:30
CIM
6403
AAM
6::0
Soundscape
261
4:18
6:64
646
CMM
Whhles
603
620
130
0008
A00
680
6an
Birds Only
439
6.03 (Bloacoustic Only)
Figure 5: Detailed transfer of training data to taxa and tasks in the BirdSet, Individual Identifica- tion, and Vocal Repertoire Discovery benchmarks. Heatmap shows the performance change for an EfficientNet trained on each data mix as compared to baseline "bio" dataset. Bio + General" is trained on only AudioSet, "+ Soundscape" adds soundscape datasets," " Whales" ablates all marine mammal recordings, "Birds only" removes all non-bird recordings.
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Table 6: BEANS Classification datasets only (best per metric in bold). We report R-AUC for retrieval; probe accuracy; clustering reported as NMI. Models above the midrule are exist- ing/ pretrained checkpoints: below are new models from this work.
Watkins
CBI
HBDB
BATS
Dogs
ESC-90
Model
Probe RAAUC NM/
Profe R-ADC
NMT
Probe
R-AUC
NM/
Prote
R-AUC
NMI
Pribee
R-AUC
NMT
Probe
R-AUC
NMF
BEATS (SFT)
0.820 0.775 0.610
0.352 0,310
0.567
0.769
0.702
0.391
0.639
0.614
0.184
0,842
0.647
0.350
0,945
0.984
0.921
BEATS (pretraimed) 1t
0.03 0.806
0.691
0.359
0.679
0.564
0,810
0.702
0.564
0.705
0.635
0.191
0.935
0.665
0.427
0.930
0.917
0.813
EAT-bbase (pretrained)"
0.850
0,744
0.585
0.247 0.617 0.502
0.778
0.630
0.482
0.635
0.588
0.125
0.705
0.585
0.194
0.858
0.884
0.655
EAT-base (SF Ty
0.67
0.808
0.613
0.388 0.714
0.558
0,782
0.686
0.528
0.631
(.899)
0.659
0.216
0.958
(.992
0.938
Bird-AVES -8---iiox-
0.52 0.703
0.556
0,318 0.63 0.521
0.769
0.594
0.455
0.662
0.593
0.091
0.770
0.585
0.253
0.858
0.791
0.624
NatureBEATS 4L-80
0.872
0.761
0.580
0.586
0.804
0.751
0.503
0.648
01885
0.684
0.466
0.912
0.51
0.7%8
Bird-MAE. MugeH S0
0.888 0.744
0.567
0.457
0.623
0.537
0.829
0.695
0.470
0.733
0.500
0.083
0.827
0.577
0.264
0.860
0.823
0.691
SurfPeech
0.570
0.756
0.687
0.578
0.664
0.309
0.921
BirdNet
0.897
0.826
0.616
0.702
0.835
0.661
0.782
0.734
0.488
0,706
0.655
0.225
0.585
0.704
0,490
0.805
0.878
0.660
Perch"
0.831
0.565
0.792
0.568
0.669
0.611
0.187
0.928
0.758
0.556
0.703
EINNeDD-Andisset"
0.708
0.759
0,753
0.235
0.660
0.531
0.732
0,666
0,310
0,566
0.621
0.155
0.7999
0.641
0.312
0.868
0.969
0.852
EITNetBa-hic
0.762
0.780
0.768
0.752
0.693
0.305
0.764
0.539
0.723
0,702
EIINeiBo-ol
0,900
0.899
0.723
0.772
0.910
0.772
0.750
0.710
0.436
0.6:9
0.615
0.226
0.899
0.762
0.579
0.830
0.930
0.770
EAT-ASS
0.85
0.266
0.633
0.718
0,489
0.784
0.604
0.236
EAT-bio SOL
0.223
0.752
0.574
0.350
0.629
0.514
0.758
0.701
0.455
0.639
0.596
0.151
0.863
0.583
0.196
0.740
0.782
0.568
EAT-allss
0.873
0.773
0.688
0.326
0.644
0.516
0.291
0.722
0.475
0.612
0.162
0.755
0.593
0.227
0.853
0,578
0.689
a--BEATS-bia M.s8
0.935
0.911 0.786
0.798
0.933
0.801
0.775
0.702
0.470
0.696
0.656
0.205
0.942
0.730
0.499
0.897
0.934
0,505
0.14
0.8%6
0.781 0.789
0,931
0.788
0.789
0.718
0.488
0.681
0.654
0.906
0.730
0,299
0.912
0.949
0.849
st-EAT ATT-bio
0.903
0.945
0.840
0.818
0.941
0.829
0.754
0.685
0.407
0.657
0.626
0.170
0.871
0.690
0.407
0.778
0.865
0.720
stEAT-all
0.885
0,952
0.76]
0,755
0.943
0,802
0,754
0.657
0.340
0.650
0.635
0.183
0,563
0,681
0.384
0,818
0,895
0,747
Table 7: BEANS Detection datasets only (best per metric in bold). We report R-AUC for retrieval and mean-average precision for probe. Models above the midrule are existing/pretrained check- points; below are new models from this work.
enabirds
rfex
hiceas
gibbons
dease
Model
Probe R-AUC|
Probe R-AUC
Probe R-AUC
Probe R-AUC
Probe
R-AUC
BEATS (SFT)SS)
0.428
0.643
0.094
0.713
0.577
0.584
0.216
0.673
0.381
0.847
BEATS (pretrained) SSL
0.525
0.678
0.110
0.720
0.544
0.627
0.351
0.686
0.373
0.897
EAT-base (pretrained) sSL
0.403
0.631
0.077
0.706
0.475
0.564
0.041
0.660
0.265
0.899
EAT-base (SFT)SL SLL
0.467
0.672
0.106
0.709
0.541
0.584
0.247
0.699
0.430
0.904
A-WVVE-iioo-bbaaaeeeeeee-e SSL
0.465
0.646
0.111
0.711
0.472
0.612
0.344
0.626
0.309
0.850
NatureBEATs SL-SSL
0.601
0.714
0.124
0.764
0.596
0.624
0.159
0.627
0.447
0.893
Bird-MAF-Hugel
0.572
0.656
0.116
0.690
0.496
0.545
0.219
0.626
0.367
0.884
SurfPerch SL
0.465
0.598
0.131
0.714
0.443
0.595
0.083
0.609
0.383
0.803
Mi
0.648
0.743
0.148
0.747
0.431
0.532
0.279
0.584
0.455
0.827
Perch
0.610
0.643
0.149
0.783
0.464
0.530
0.252
0.622
0.365
0.792
EUNNedBO-AudioSet
0.343
0.627
0.060
0.679
0.398
0.561
0. 145
0.589
0.285
0.893
ENINetBO-bio SL.
0.501
0.701
0.120
0.732
0.486
0.521
0.258
0.643
0.459
0.879
EANNeCB--allS
0.528
0.692
0.129
0.736
0.505
0.555
0.166
0.678
0.482
0.901
EAT-ASSSL
0.418
0.654
0.086
0.717
0.534
0.579
0.255
0.665
0.263
0.903
SAT- SSL
0.428
0.660
0.087
0.665
0.571
0.515
0.081
0.667
0.389
0.890
SSL
0.475
0.668
0.103
0.723
0.569
0,511
0.155
0.666
0.275
0.901
S--BEATS-bio SL-SSL
0.555
0.712
0.109
0.750
0.536
0.571
0.303
0.667
0.448
0.897
3-BEATS-alISLSSE
0.566
0.716
0.118
0.741
0.527
0.566
0.366
0.700
0.465
0.906
d-EAT-bioSLLS SL-SLL
0.516
0.666
0.099
0.708
0.546
0.580
0.190
0.638
0.415
0.842
sl-EAT-al SS-
0.528
0.665
0.099
0.739
0.536
0.618
0.170
0.667
0.445
0.832
C.3 FULL RESULTS
We include full results for each benchmark in Tables 6 (BEANs Classification) 7 (BEANS Detec- tion) 8 (BirdSet) and Table (Vocal Repertoire and Individual ID).
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Published as a conference paper at ICLR 2026
Table 8: BirdSet benchmark results: Multi-label bird detection tasks (best per metric in bold). We report ROC AUC for retrieval as R-AUC and mean-average precision for probe. No clustering metries are reported. BBirdNet results are excluded following the authors (Rauch et al. 2025b). Models above the midrule are existing/pretranee checkpoints; below are new models from this work.
POW
PER
NES
NBP
HSN
SNE
UHH Probe
Model
Probe R-AUC| Probe R-AUC Probe R-AUC| Probe
R-AUC|
Probe R-AUC
Probe R-AUC
R-ACC
BEATS (SFT)
0.108 0.654
0.046
0.642
0.062
0.726
0.213
0.649
0.1007
0.625
0.079
0.725
0.094
0.704
BEATS (pretrained) sst.
0.157
0.703
0.070
0.649
0.095
0.731
0.248
0.648
0.105
0.568
0.116
0.753
0.109
0.751
EAT-base (pretrained) sst
0.137
0.658
0.053
0.621
0.064
0.712
0,188
0.627
0.094
0.548
0.092
0.679
0.098
0,706
EAT-buse (SFT) St.s.L
0.163 0.649
0.066
0.634
0.097
0.745
0.290
0.651
0.140
0.585
0.124
0.741
0.124
0.724
Bird-AVES-biox base Sa.
0.142 0.679
0,044
0.615
0.050
0.755
0.196
0.631
0.050
0.556
0.082
0.714
0.081
0.740
NatureBEATS $1-381.
0,244 0.722
0.132
0.690
0.177
0.819
0.419
0.708
0.251
0.574
0.197
0.796
0,143
0.749
Bird-MAE-Hupg Hugee SS.
0.243 0.718
0.092
0.621
0.148
0.686
0,314
0.599
0.104
0.527
0.132
0.618
0.141
0.686
SurfPerch st.
0.186
0.691
0.067
0.619
0.151
0.811
0,252
0.639
0.183
0.582
0.120
0.747
0.164
0.766
Bir
N/A
NIA
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Perch
0.236
0.686
0.132
0.626
0.341
0,803
0.374
0.595
0.183
0.512
0.160
0.658
0.203
0.713
ERNNBBO-Andisett St.
0.115
0.637
0.045
0.569
0.054
0.728
0.181
0.615
00.087
0.609
|.0087
0.725
0..15
0.701
EATNetBO-bio St.
0,283
0.717
0.621
0.263
0.832
0.454
0.717
0.383
0.544
0.212
0.738
0.231
0.759
EfINetBO-alI
0,276
0.719
0.137
0.616
0.273
0.845
0.473
0.727
0.375
0.525
0.196
0.770
0.220
0.749
EAT-AS
0.147
0.698
0.060
0.638
0.074
0.761
0,230
0.646
0.138
0.588
0.112
0.723
0.114
0.739
EAT-
0.214
0.658
0,069
0,618
0.105
0.662
0,257
0.637
0.119
0.542
0,114
0.657
0.125
0.642
EAT-all
0.188
0.702
0,065
0,649
0.113
0.731
0,303
0.648
0.585
0,568
0,147
0.708
0.158
0.734
sl-BEATS-bio
0.304
0.707
0,150
0.629
0.279
0.836
0.496
0.737
0.349
0.627
0.226
0.766
0.213
0.781
a--BAASS-al St.st.
0.322
0.720
0.152
0.612
0.257
0.834
0.493
0.737
0.404
0.640
0.211
0.786
0.221
0.796
slFEAT-bio 4-S81.
0.274
0.670
0.143
0.596
0,224
0.813
0.436
0.713
0.283
0.636
0.190
0.760
0.191
0.748
sI-EAT-all SSS
0.265
0.700
0.129
0.600
0.219
0.828
0.452
0.707
0.328
0.586
0.192
0.760
0,203
0.763
Table Complex bioacoustic tasks: Individual ID and Vocal Repertoire analysis (best per metric in
bold). We report ROC AUC for retrieval as R-AUC. Individual ID probe is accuracy; Vocal Reper- toire reports both R-AUC and NMI. Models above the midrule are existing/prerrained checkpoints; below are new models from this work.
chilfchalf-cros litloowsscroos
pipil-cross Prifer R-AGC*
sebraainch--e-call Otters Bengalese_Fing SRKW.Or Wrre
Model
Prode RAAUC*
Probe R-AUC*
Probe
R-AUC
R-AUC
NM1
|RAFC
NMI
|RA0C
NM/
|RAUCC
NMI
BEATS (SF
10.85
0.470
0.290
0.663
[0006]
0.500
0.963
0.775
0.651
0.295
0.815
0.545
0.898
0.742
0.657
0.359
BEATS (pretrained) Pu
0.180
0.536
0.263
0.300
0:093
0.486
0.827
0,307
0.352
0.573
0.848
0.697
0.409
EAT-base (pretrained)" pms.
0.205
0.544
0.317
0.676
0.058
0.169
0.872
0.804
0.681
0.231
0.788
0.503
0.974
0.571
0,626
0.265
EAT-base (SFT)" sa.
0.245
0.511
0.391
0.054
0.981
0.742
0.855
0.591
0.964
0.687 0.357
Bird-AVES hie
0.230
0.521
0202
0.634
0.118
0.492
0.967
0.840
0.660
0.255
0.751
0.484
0.872
0.757
0.621
0.318
NatureBEATs SL-S..
0.185
0.489
0.359
0.112
0.524
0.984
0.704
0.351
0.862 0.566
0.943
0.736
0.438
Bird-MAE-Hugee
0.195
0.500
0.361
0.706
0.104
0.497
0.956
0.841
0.678
0.278
0,998
0.519
0.917
0.111
0.653
0.331
SurfPerch"
0.280
0.550
0.383
0.713
0.179
0,518
0.986
0.843
0.626
0,225
0.810
0.537
0.959
0.927
0.608
0.279
BindNee
0.200
0.555
0.501
0.301
0.204
0.558
0.984
0.916
0.707
0.378
0.798
0.539
0.987
0.911
0.689
0.353
0.210
0.500
0.649
0.547
0.288
0.570
0.973
0.504
0.657
0.284
0.854
0.585
0.959
0.896
0.206 0.561
HENNeBO-AndloSer"
102255
0.506
0.290
0.109
0.492
0.966
0.823
0,701
0.354
0.760
0.438
0.966
0.611
0.270
EdINelDO-bio"!.
0.140
0.532
0.346
0.730
0
0.557
0.982
0.912
0.717
0.350
0.828
0.574
0.964
0.503
0.717
0.443
EII
0:273
0.516
0.196
0.742
0.748
0.441
EAT-ASS:
0.165
0.544
0.251
0.641
0.075
0.473
0.957
0.848
0.707
0.301
0.853
0.566
0.977
0.885
0.688
0.379
EAT-b8
0.175
01540
0.307
0.115
0.474
0.914
0.8014
0.654
0.246
0.543
0.934
0.316
0.630
0.260
0.200
0.547
0.152
0.575
0.109
0.487
0.929
0.836
0.709
0.335
0.820
0.549
0.997
0.347
0.646
0.321
--BATT--ii
0.235
0.558
0.339
0.722
0,390
0.570
0.972
0.873
0,700
0,369
0.840
0.572
0.830
0.675
0.735
0.418
-I- sor
0.225
0.574
0.113
0.755
0.428
0.580
0.977
0.850
0.718
0.426
0.832
0.554
0.897
0.681
0.746
0.457
SLEAT-
0.245
0.532
0.474
0.702
0.281
0.572
0.980
0.882
0.703
0.381
0.817
0.540
0.989
0.937
0.716
0.402
0.195
0.509
0.354
0.688
0.326
0.557
0.949
0.795
0.718
0.338
0.501
0.980
0.203
0.383
C.4 BEANS CLASSIFICATION DATA MIX WITH/WITHOUT ESC-50
The BEANS benchmark contains two auxiliary non-bioacoustics datasets ESC-50 and SpeechCom- mands. We excluded SpeechComman because this dataset is irrelevant for non-human bioacous- ties applications and speech is a well researched area beyond bioacoustics. In contrast, we included ESC-50 because general sound classification is useful for some conservation tasks like habitat clas- sification, poaching monitoring (gunshots, explosions) and this is the reason why we included it in Tables 3 To disentangle the effects of the data mix and answering the question: "does including general sound in training give better representation?" we report the averaged results without ESC-50. We include the original tables side-by-side for comparison.
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Performance on BEANS Classificatiin (wio ESC-50) vs BEANS Detection separates by training paradigm
Training Approach
TS-BEgTS-all
BEATS NerTs-NttteeeMMuun
New SL
BEATS (ngetrained)
0.72
Existing SL
sl-BEgTS-bie
New SSL
Existing
EAT-bage (SFT)
BD-Nl
Retrioval BEANS Detection R-AUC
Post-trained SSL
0.71
EATAS
sl-E@T-all
EffNeg@0-bio
EAT
EAT-base (gretrained)
BEATS (SFT)
0.09
Bird AVES
AVES
BirgNet
SI-E-T-bio
BirdIMEE (artialned)
0.00
Perch
EfNNetB
Surfgerch
0.625
0.650
0.575
0.200
0.725
0.750
0.775
Retrieval BEANS Classification R-AUC (w/o ESC-50)
Figure 6: Supervised encoders outperform self-supervised on BEANS classification, which is pri- marily focal recordings. However, self-supervised encoders suffer markedly smaller performance drops than supervised encoders when moving from focal recordings to soundscape (BEANS Detec- tion), showing strong duttr--distribution performance. contrast, self-supervised encoders post- trained with supervised learning on bioacoustic data enjoy the strongest performance both in and out-of distribution.
We note that the ranking per-model has not changed e.g. sI-BEATs models are better than EffNetBO models. However, there less of a gap between 'bio" and 'all' setups, with 'bio' being slightly better in some cases such as EffNetBO.
We include version Figure 3 without including ESC-50 when aggregating the results. We note that there is gap between '-all' and '-bio' models BEANS detection, with the former models having superior R-AUC.
C.5 BIRDSET DIRECT POST-TRAINING EVALUATION
We take the SL checkpoints we trained for the post-training phase and directly evaluate them on the BirdSet dataset by considering solely the logits corresponding to the datasets in BirdSet. This evaluation setup comparable the LT (large training) setup the BirdSet paper with the following additions: (1) more training data and (2) fine-tuning whole model, hence we call LT+. contrasts with the DT (dedicated training) setup which we reported initially in Table X i.e. linear probing of model on each BirdSet subset. Notably, in
DT is done top of LT+ and it shows degraded performance. Similarly to the BirdSet paper the LT, and aforementioned LT+, have better results than DT.
Why LT+ better than DT for BirdSet? Both of the setups contain domain shift and although we add the same augmentations in LT+ and DT, DT seems to overfit to often small training set originat- ing in Xeno-Cante Cantto, whereas LT+ sees more data, and learns to discriminate with high granularity a high number of classes by learning the fime-frequency priors, useful for the domain shift.
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Aggregate results for BEANS Classification with and without ESC-50. We report ROC AUC for retrieval, accuracy for probing on BEANS classification. We report the mean of each metric over datasets per benchmark. Model labels carry training tags: SSL self-supervised. SL su- pervised, SL-SSL supervised fine-tuning after SSL pretraining. Models above the midrule are exist- ing/pretrained checkpoints; below are new models from this work.
Table 10:
BEANS Classification (w/o ESC-50)
BEANS Classitication (w/ ESC-50)
Model
Probe R-auc
C-nmi
Probe R-aue
C-nmi
BEATS (SFT)$S
0.680 0.690
0,420
0.724 0.739
0,504
BEATS (pretrained)" sst.
0.742
0.488
0.774 0.734
0.542
EAT-base (pretrained)SSi
0.643 0.633
0.378
0.679 0.675
0.424
RAT-base (SFT)
0.718 0.700
0.386
0.758 0.748
0.478
Sond-AVSS-bio--base St.
0.674 0.618
0.367
0.705 0.646
0.410
NatureBEATs SL-SSL
0.783 0.738
0,512
0.804 0.774
0,560
SurfPerch
0.733 0.710
0.426
0.760 0.745
0.484
BirdNet St.
0.794 0.751
0.496
0.796 0.772
0.523
Perch
0.757 0.729
0.432
0.768
0,759
0.478
Bird-MAE-Hugee
0.747 0.644
0.380
0.766
0.674
0.432
MTNNeBO-AudioSee
0.608 0.671
0.412
0.651
0.721
0,486
EfINetBO-bio
0.798 0.780
0.536
0.786
0.799
0.563
EHNetBB--alst.
0.794 0.785
0.547
0.800
0.809
0.584
EAT-ASSSI.
0.672 0.678
0.419
0,704
0,714
0,473
EAT-bio SSL
0.683 0.648
0.378
0.692
0.671
0.410
SAT-alISS
0.680
0.669
0.400
0.709
0.704
0.448
N-BEATS-bio SLSSE.
0.829
0.786
0,552
0,840
0.811
0,594
3-BEATS-all
0.816
0.786
0.555
0.832
0.813
0.604
M-EAT--bio SL.
0.801
0.777
0.531
0.797
0.792
0.562
SL-E.A--IIILSSSL
0.781
0.770
0.494
0.788
0.791
0.536
Table 11: BirdSet benchmark results: Comparison of post-training (LT+) vs adding a dataset-wise probing afterwards, on top of post-training (DT) for sl-BEATS models.
Model
POW
PER
NES
NBP
HSN
SNE
UHH
Sl-BEATS-bio (DT)
0.304
0.150
0.279
0.496
0.349
0.226
0.213
BE-BEATS-bio (LT+)
0,355
0.167
0,372
0.535
0.377
0.261
0.271
-L-BEATS-all (DT)
0.322
0.152
0.257
0.493
0.404
0.211
0.221
sI-BEATS--ll (LT+)
0.343
0.167
0.356
0.535
0.406
0.268
0.224
C.6 PROBING ABLATION LINEAR VS ATTENTION
In our initial evaluation, the embeddings extracted from the model are averaged the time axis. To model the temporal dependencies between the embeddings we evaluate some of the models on the BEANS and BirdSet benchmarks using an attention-based probe.
The attention has a single multi-head self-attention layer on top of the extracted backbone represen- tations. The output of this attention operation is added back to the original embeddings through residual connection, followed by layer normalization and optional dropout. The resulting sequence then aggregated by taking the mean across tokens, and finally passed through a linear classifier to produce the prediction.
For fair comparison and to reduce computational cost we train both probe heads for each dataset in BEANS and BirdSET for 50 epochs, instead of the 900 used in our initial experiments. To reduce overfitting, we introduced a cosine learning rate scheduler with the first 5 epochs being the learning learning rate of 0.0001 and AdamW optimizer. The BirdSet models trained
stage. We with the noise and mixup augmentations introduced in Section 3.4
The results presented in Table show that in general attention probes have superior performance to linear probes. This slightly alters the ranking of the models, e.g. BEATs (pretrained) is now the top model on BEANS Detection, EffNetBO is surpassed by the transformers, si-BEATs-all is still
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Classification
Detection
BirdSet
BEATS (38L)
0.79
0.71
0.44
0.27
0.16
0.10
Hoture ANGUESBEATS (SL NE-S5LL
0.78
0.60
0.37
0.25
0.21
0.11
EAT-SINSO (SSL)
0.77
0.32
0.15
0.12
EAT- FONNO (SRT)
0.76
0.47
0.35
0.22
0.12
0.07
AVES
0.57
0.32
0.26
0.11
0.10
0.06
EnNettO a0 (SL)
0.72
0.73
0.21
0.22
0.18
0.18
EAT-Bll
0.77
0.51
0.39
0.23
0.16
0.11
a1-sAT-all
0.80
0.70
0.34
0.27
0.24
0.17
(5L-5591)
WEATS- all (SL-80L)
0.80
0.80
0.40
0.38
0.26
0.25
Attention
Attention
Linear
Attention
Linear
Figure 7: Linear vs Attention probing comparison. The results are aggregated across bioacoustic benchmarks and tasks. We accuracy for BEANs Classification, avenraa precision for BEANs Detection and BirdSet. We report the mean of each metric over datasets per benchmark Model labels carry training tags: SSL self-supervised, SL supervised, SL--SSL supervised fine-tuning after SSI. pretraining. PT denotes pretrained. Models above the red line are existing checkpoints; below are new models from this work.
of the best models, EAT models improve lot with the attention head. There are less differences between the top and the bottom models in the ranking.
SSL models benefit ics of SSL models, which emphasize capturing temporal structure in audio rather than developing species-specific inductive biases, is more common in supervised learning. Consequently, when the backbone is a transformer trained via SSL, pairing it with transformer-basee probe is more ef- fective, as it better aligns with and leverages the backbone's 's representational properties. Moreover, for EffNetBO-all and A-BBATTs-al the attention probes did not yield considerable gains.
from attention head. This effect may stem from the training dynam-
26


Reviews:
[{"review_id": "o5MGQc0c6G", "reviewer": "Reviewer_Jxj8", "summary": "The paper is an empirical study of bioacoustic encoder. The paper explores several benchmarks and training configurations.", "questions": "Would it be possible to better articulate if there are contributions along these questions and what those contributions are?\n- what new technical advances are needed? \n- Is there a new dataset being introduced?\n- Is there a better definition of the task being proposed? \n- Are there new evaluation metrics being introduced?\n- Is there a new problem/task being introduced?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The overall goal of studying bioacoustics is fascinating and important. I think it is a great application domain for the NeurIPS community.", "weaknesses": "The contribution is interested by limited in several ways. The reported results will be interesting for the community working on this area, but might not be interesting for a wider set of researchers. An experimental paper would need to introduce challenges derived from bioacustics that can motivate technical advances, or introduce a new dataset, or a better definition of the task with new evaluation metrics, or solving a new problem. But the current contribution is limited along all those dimensions. \n\nThe technical novelty is limited. \n\nSome of the results are not very surprising such as\n- adding general audio into the training significantly improves model transferability\n- self-supervised models under-perform supervised models\nBoth those results are not unexpected. \n\nThe approach uses standard evaluation metrics (linear probing, retrieval, and clustering) and tasks (classification and detection) used by the community. Although some of those are extensions of existing benchmarks, on its own are a limited contribution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QCNBbBBmPB", "reviewer": "Reviewer_B3CZ", "summary": "This paper focuses on building strong bioacoustic embedding models that can be used for a wide range of ecological applications including species ID, individual ID and repertoire discovery. The authors explore supervised and self-supervised methods with a focus on data diversity and scale to suggest a training recipe that works well for bioacoustics tasks, particularly when transferring to novel domains. They benchmark existing methods extensively across 26 datasets, different model architectures, training recipes and data distributions. Part of this benchmarking are the individual-ID and repertoire discovery tasks for which the authors propose newly curated evaluation datasets. Based on their results, the authors recommend training recipes and best practices for learning strong bioacoustic embeddings.", "questions": "1. I am curious about the Individual ID and Vocal repertoire discovery tasks. In the post training stage, different call types and individuals of the same species should have been assigned the same label, so I imagine the model should be learning similar embeddings for different individuals and calls of the same species. How do the authors explain this “emergent” ability? Is self-supervised learning the main contributor? Although for Individual ID, Perch and BirdNET seem to be doing on par and maybe even better without any self-supervision.   \n2. The takeaways about the benefits of self-supervised learning are counter to the observations in Perch 2.0 \\[1\\]. Could the authors suggest possible factors to explain this difference.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The paper includes extensive experiments with multiple model architectures, different training recipes and a number of evaluation tasks, both well established and newly proposed.   \n2. The paper presents a strong benchmark unifying different existing approaches. Different works often follow different evaluation strategies in bioacoustics; and the baselines and evaluation protocol in this paper could serve as a good standardized benchmark and reference for future work.   \n3. The architecture, pre/post-training recipes and design choices for evaluation tasks are sufficiently detailed and well-motivated.  \n4. The newly curated tasks for individual ID and repertoire discovery are quite interesting and can help understand the ability to capture details at finer granularities than the already fine-grained species identification.", "weaknesses": "Overall, I believe this work has the potential to be a strong contribution to the community and commend the authors’ effort. However, I think there is still room for improvement in several key areas before publication. \n\n\n**\\[Novelty\\]**\n\n1. The technical novelty of the paper is relatively low. Separately, self-supervised learning has been explored by BirdMAE, mixing bioacoustic data with general audio data has been explored by Perch 2.0 \\[1\\] (although this is more of a concurrent work), and effects of including non-avian species have also been explored by iNatSounds and SurfPerch. \n\n**\\[Standardized benchmarking\\]**\n\n2. For BirdSet, if I understand correctly, the authors use train splits for each datasets and learn linear probes. Given that the training dataset includes XenoCanto, I suspect that most if not all species in BirdSet tasks should be a part of the training dataset. Can the authors also report performance with the classifier head learnt during post-training? I believe the latter was used by the BirdSet authors, and a consistency in evaluation can be helpful for future work. These extra numbers could just go in the supplementary if the authors would like to preserve the current flow.  \n\n3. For BEANS classification tasks, if I understand correctly, the authors use “time-averaged embeddings”, but BEANS benchmark uses the first window only. I think the authors’ choice is more reasonable, but again argue in favour of consistency in evaluation. I understand that the baseline results presented by the authors are consistent with each other, but it would be helpful for future work to have easily comparable results.   \n4. For BEANS classification tasks, looking at the supplementary table 8, it seems like the authors include ESC-50, but not speech commands. This choice affects the mean-BEANS metrics and should be justified in the main text. ESC-50 is also missing in Table 5-6. \n\n**\\[Results\\]**\n\n5. All rows in Table 3 are repeated as-is in Table 4\\. This seems unnecessary to me. I ask that the authors either keep them in a single table or remove the rows from Table 4\\. I would recommend the former. I understand the benefit of the distinction of these results, but perhaps the authors can use a different background color or some other way to highlight the Table 3 numbers.   \n6. The authors choose to use WABAD as part of the training in an ablation. Since this is a densely labelled soundscape dataset, I believe this could be more useful as an evaluation dataset instead.   \n7. In Table 8, I was looking at “sl-BEATs-bio” and “sl-BEATs-all”. It seems like using AudioSet helps with ESC-50 considerably. Given the smaller aggregate improvement in Table 4, I take this to mean that including Audioset actually worsened the performance for the aggregate of the rest of BEANS classification tasks, which are the actual bioacoustics tasks. This seems counter to the claims of the benefit of training on general audio. The considerable improvement in ESC-50, which contains environmental sounds, may be partly due to similar sounds in AudioSet and may actually be misleading as an evaluation for bioacoustics tasks.    \n8. I am unclear about the motivation for Table 2b. As per my understanding, while the BEANS classification tasks have focal recordings, they still have a domain shift with regards to the species being considered. For example, aquatic species in watkins and insects in HumbugDB. The detection tasks also have this “label shift”, but for different species. To me, this is a confounding factor and I am unclear if the difference in behavior for the two can be attributed to the focal vs soundscape domain shift. Following the previous point, if ESC-50 is included in classification tasks, this is another confounding factor when assessing the effect of adding AudioSet. \n\n**\\[Minor Points\\]**\n\n9. Elements of the paper are going out of the margin in a few places (Table 2, Fig 4 and 5\\)  \n10. Fig 2: font size of text is too small  \n11. Line 316 “ADD REF TO AGILE PAPER” :)   \n12. Line 213: I believe Perch is trained from scratch instead of starting from ImageNet. Not critical to the discussion, but may be worth noting.    \n13. The notation in Table 4 is confusing. What is the prefix “sl-E…” in sl-EAT-all$^{SL-SSL}$?  \n14. Notation (superscripts) is different in Table 8 vs Table 4\n\n**References**\n\n\\[1\\] van Merriënboer, Bart, et al. \"Perch 2.0: The Bittern Lesson for Bioacoustics.\" arXiv preprint arXiv:2508.04665 (2025).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DgDwojDxXZ", "reviewer": "Reviewer_WxmL", "summary": "The paper investigates the factors that contribute to effective bioacoustic\nencoding by evaluating design choices like data sources, model architectures and\ntraining strategies. The authors therefore conduct an empirical study comparing\nmultiple pre-training datasets (general audio recordings with AudioSet, birds\nfrom Xeno-Canto, Sapsucker Woods and WABAD, diverse animal sounds from\niNaturalist and Animal Sound Archive, marine mammals from Watkins), model\narchitectures (EfficientNet, ViT) and training strategies (supervised pre- and\npost-training, self supervised pretraining with EAT). Evaluation is carried out\non the BEANS and BirdSet benchmark and two additional tasks of individual\nidentification and vocal repertoire discovery. Generally, models benefit from\ndiverse pretraining data (also including AudioSet and not only bioacoustic\ndata). In addition, supervised posttraining improves performance for\nSSL-pretrained models. The authors wrap these findings in a recipe for post-training SSL-backbones.", "questions": "- How were the hyperparameters selected? Why are different LR used for BEATs and EAT? What HP are used for linear probing?\n- How many seeds were used for the experiments? What are the standard deviations?\n- Which BEATs checkpoint was used?\n- Do you plan to release the training and evaluation code?\n- Analysis of the \"curation level\" of the datasets would be interesting as initially analyzed in [1]. How many samples per class are needed, how diverse should the data be? Have you considered analyzing this aspect?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Research topic is relevant and motivated well.\n- Contributions are clearly stated.\n- Experimental design is comprehensive, covering multiple datasets,\n  architectures, and training strategies.\n- Results are novel but not entirely surprising.", "weaknesses": "- For a systematic comparison of model architectures and training paradigms the study lacks SSL models based on CNN architectures and SL models based on ViTs. This would help to better disentangle the effects of architecture and training paradigm.\n- To simplify from the results of one SSL-pretrained model (EAT) to SSL in general is a bit of a stretch. Including more SSL training techniques would strengthen the claims.\n- The evaluation protocol only considers the \"clip\" embeddings and leaves the patch embeddings for future work. [1,2] shows that the general audio models compare better when using patch embeddings with attentive probing.\n- BirdMAE as a strong bioacoustic SSL baseline is missing in the comparison.\n- The 2 and 3.4 would benefit from paragraph headings to improve readability.\n- Glitch in l. 316\n\n[1] Can Masked Autoencoders Also Listen to Birds? https://arxiv.org/abs/2504.12880\n[2] Foundation Models for Bioacoustics -- a Comparative Review\nhttps://www.arxiv.org/abs/2508.01277", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xwFRQLjvUi", "reviewer": "Reviewer_ykFi", "summary": "This paper presents a large-scale empirical study to identify \"what matters\" for training bioacoustic encoders. It investigates the impact of model architectures, training data composition (bioacoustics-only, general audio, and mixed corpora), and training paradigms (SL, SSL /pre-training and post-training recipes). The authors collect a large-scale pre-training dataset and evaluate on an extended evaluation set. Their key finding is that a recipe of self-supervised pre-training on a mixed bioacoustic and general audio dataset, followed by supervised post-training, yields best performing models in- and out-of-distribution tasks.", "questions": "1. Could you provide a clearer rationale for your selection of training datasets and baseline models? Specifically, why was (the older) BEATs chosen over other reproducible open-source SSL models?\n2. Could you clarify some details of the evaluation? (a) How is the \"win-rate\" metric in Figure 2 formally defined and calculated? (b) Were statistical significance tests (or the mean of some repetitions) performed to validate the superiority of your proposed recipe? (c) Why was only linear probing used and not other methods that might better utilize the learned representations (and change results)?\n3. To ensure its impact and allow for verification, can you provide access to the code for the review process? Furthermore, what is your concrete plan for releasing not just the model checkpoints, but (maybe) also the curated data splits or the full evaluation framework to the community?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- **Comprehensive and valuable empirical study**: The primary strength of this work is its ambition and scope. The paper tackles an important question in the bioacoustics community by systematically comparing different modeling components. This kind of large-scale, well-structured empirical study is valuable for future research.\n- **Expanded evaluation framework:** The authors curate and introduce a broader evaluation benchmark.  The paper moves beyond species classification and includes tasks like individual ID and vocal repertoire discovery. It also augments existing benchmarks with clustering and retrieval metrics, providing a potentially more realistic assessment of an encoder's performance.\n- **Actionable insights:** The paper provides actionable results for training robust bioacoustic encoders. The finding that a combined self-supervised and supervised approach on a diverse data mix yields the best overall performance is an important insight.", "weaknesses": "While the findings of the paper are valuable, its contribution is limited by its presentation, methodological choices/novelty, and questions about its reproducibility. A short disclaimer: Given that the paper's contribution is not methodological, the focus necessarily shifts to the quality and execution of its empirical investigation which poses some questions.\n\n**[Presentation and readability]**\n\n- Clarity of figures and tables: The presentation of results could be significantly improved. Figure 2 is difficult to read due to its small size. The tables, particularly Tables 2, 3 and 4, use a complex system of abbreviations that makes them hard to parse and compare models at a glance. A more structured in-table organization with sub-information could help here.\n- Lack of structure in text: Long sections of text, such as the Related Work and Proposed Models or Evaluation Setup sections, would benefit greatly from some kind of sub-headers. This would improve readability and help the reader navigate the information being presented.\n- Formatting issues: A few minor formatting issues exist, such as Figure 1 being over the text height, placeholders like \"ADD REF TO AGILE PAPER\" (L.316), and Figure 2 being very hard to read.\n\n**[Methodological Choices]:**\n\n- Dataset and model selection: The paper lacks a clear motivation for the specific choice of datasets in the training corpus and the selection of models for comparison. For example, the rationale for choosing BEATs, for which the official training implementation is unavailable, over a more recent and reproducible open-source model like SSLAM from last year’s ICLR is not provided.\n- Incomplete baselines: While the study includes many models, it omits direct comparisons with SSL bioacoustic models mentioned in the related work. Including these would provide a more complete picture and better contextualize the performance of the proposed recipes as the respective baselines.\n\n**[Evaluation details and reproducibility]:**\n\n- Lack of statistical and methodological detail:  It is unclear how the \"win-rate\" in Figure 2 is calculated or whether any statistical significance testing  (or repetitions) was performed to validate the results. Furthermore, the process for hyperparameter selection across the many models and training recipes is not described, making it difficult to assess the fairness of the comparisons.\n- Limited probing techniques: The evaluation relies exclusively on linear probing. While standard, recent work in self-supervised learning in computer vision has shown attentive probing can provide a more nuanced view of an embedding's quality, and its omission here is a missed opportunity. If there were more methodological contributions, this would not be a problem but it would greatly strengthen the contribution of an empiricial study.\n- No code or release plan: For a study of this complexity, reproducibility is important, but there is no code available during the review process. The promise to release checkpoints \"upon paper acceptance\" is not enough. There is also no clear plan for releasing (if it happens) the curated datasets, splits, and evaluation protocol to establish this work properly.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)