Bioacoustics

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11 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 48

Jun 11, 2026cs.LG

Decoding Insect Song: A Multitask Semisupervised Orthoptera Bioacoustic Classifier

Passive acoustic monitoring holds great promise for ecological inference, yet existing automated tools are typically narrowly trained and non-transferable. We address these limitations with PULSE, a semi-supervised, multi-task framework for Orthoptera bioacoustics, combining weakly-supervised species classification, self-supervised learning on unlabelled field audio, and knowledge distillation from a general-purpose bioacoustic model. Our domain-adapted specialist model outperforms a state-of-the-art general model across all metrics (macro F1: 0.21 vs. 0.07; AUC: 0.74 vs. 0.45; AP: 0.32 vs. 0.19), with active learning further raising F1 to 0.34 and AUC to 0.84. Beyond classification, the learned embeddings encode ecologically meaningful structure, exposed through an interactive visualisation tool for ecological discovery.

Olga Isupova, Danil Kuzin, Ella Browning +2

Leverhulme Centre for Nature Recovery, School of Geography and the Environment, University of Oxford, Oxford, UK

Audio Representation Learning Audio Understanding Classification Bioacoustics Passive Acoustic Monitoring Semi-Supervised Learning+2

Jun 10, 2026cs.LG

Dolph2Vec: Self-Supervised Representations of Dolphin Vocalizations

Self-supervised learning (SSL) has opened new opportunities in bioacoustics by enabling scalable modeling of animal vocalizations without the need for expensive manual annotation. However, current SSL models in this domain prioritize broad generalization across species and are not optimized for uncovering the fine-grained structure of individual communication systems. In this work, we collect and release a novel dataset of over five years of longitudinal recordings, from five known dolphins in a semi-naturalistic marine environment, an unprecedented resource for studying dolphin communication. We adapt the Wav2Vec2.0 Baevski et al. (2020) architecture to this domain and introduce Dolph2Vec, the first large-scale, species-specific SSL model trained exclusively on this data. We benchmark our model on two biologically relevant tasks: signature whistle classification and whistle detection. Dolph2Vec significantly outperforms general-purpose baselines in both tasks. Beyond performance, we show that learned embeddings and codebook structure capture interpretable acoustic units aligned with dolphin whistle categories and possibly sub-whistle structure, enabling fine-grained analysis of communication patterns. Our findings demonstrate how SSL can serve as both a model and a scientific tool to explore hypotheses in animal communication research.

Chiara Semenzin, Faadil Mustun, Roberto Dessi +5

École Normale Supérieure, Paris, France · Not Diamond, San Francisco, USA · Institut du Cerveau, Paris, France +1

Audio Representation Learning Audio Understanding Sound Event Detection Bioacoustics Self-Supervised Speech Representation Learning Audio Classification

Jun 9, 2026cs.SD

Time-frequency localization of bird calls in dense soundscapes

Passive acoustic monitoring enables large-scale wildlife observation. Most bioacoustic classifiers predict species presence in a time window without localizing vocalizations precisely in time or frequency, limiting downstream analyses. We formulate time-frequency localization of bird calls as object detection on spectrograms and compare three computer vision model families (YOLO11, SAM 3, RF-DETR) against a non-learnable baseline. We introduce Intersection over Minimum (IoMin), an evaluation metric that better handles ambiguous acoustic boundaries than IoU. We also open-source a browser-based tool for efficient bounding-box labeling. The best RF-DETR model nearly doubles baseline performance on in-distribution, dense soundscapes from Singapore (83.4% vs. 42.1% IoMin@50 F1-score) and generalizes better to out-of-distribution recordings from Hawaii (63.2% vs. 48.6%). These results indicate that fine-tuned computer-vision models are well suited for time-frequency localization of bird vocalizations in complex soundscapes.

Simen Hexeberg, Fanghui Tong, Hari Vishnu +1

Tropical Marine Science Institute, National University of Singapore, Singapore · Dept. of Electrical and Computer Engineering, National University of Singapore, Singapore · School of Computing, National University of Singapore, Singapore

Sound Event Detection Object Detection Bioacoustics Passive Acoustic Monitoring Wildlife Monitoring

Jun 5, 2026cs.SD

MyGardenBird: A Machine-Learning-Ready Bird Sound Dataset for Twelve Common Malaysian Birds

Bioacoustic datasets from tropical regions remain limited, in part due to the absence of reproducible workflows for aggregating recordings from public archives. We present \textbf{MyGardenBird}, a curated dataset of bird vocalisations representing twelve common species across Peninsular Malaysia and the Indo-Malayan region. Recordings were sourced from Xeno-canto and processed through species-level filtering, manual spectrogram segmentation, and quality control checks. The primary release comprises 7,200 manually validated audio clips (16 kHz, 16-bit PCM mono WAV), balanced at 600 three-second clips per species (6.0 hours total) derived from 1,381 distinct recordings. Metadata includes geospatial coordinates, vocalisation categories, and signal-to-noise ratio (SNR) values (range: 0.83--59.18 dB; mean: 15.80 dB). A supplementary 44.1 kHz version is also provided. To mitigate data leakage, dataset partitions are defined at the source-recording level. Baseline classification experiments using convolutional neural networks on Mel-spectrograms achieved test accuracies of 92--96%, indicating strong interspecies separability. Limitations include reliance on single-annotator curation; however, validation with BirdNET confirmed label consistency. MyGardenBird is openly available at https://doi.org/10.5281/zenodo.20306877 under a CC BY-NC-SA 4.0 licence. Complete preprocessing code accompanies the release to support reproducibility and future expansion.

Muhammad Mun'im Ahmad Zabidi, Mohd Yamani Idna Idris, Norisma Idris

Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia · Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia

Training Data Curation Bioacoustics

May 20, 2026cs.SD

SEABAD: A Tropical Bird Activity Detection Dataset for Passive Acoustic Monitoring

Passive acoustic monitoring (PAM) enables large-scale biodiversity assessment, but continuous recording generates large amounts of non-informative audio, creating challenges for storage, power consumption, and long-term edge deployment. Bird audio detection (BAD), which identifies bird vocalizations, can reduce this burden by filtering irrelevant recordings before downstream analysis. However, most BAD systems are trained on temperate datasets despite tropical soundscapes being denser, more species-rich, and acoustically unpredictable. To address this gap, we introduce SEABAD (Southeast Asian Bird Activity Detection), a dataset of 50,000 curated three-second clips from Southeast Asian soundscapes, evenly balanced between bird-present and bird-absent samples. The dataset spans 1,677 bird species and is standardized to 16 kHz mono audio for embedded and low-power inference. We developed a dual-branch curation pipeline: a six-stage positive-label workflow applied to Xeno-Canto recordings, alongside six source-specific negative-label extractions from environmental datasets. These procedures reduced class imbalance by 13.7% (Gini coefficient: 0.601 to 0.519). A manual audit of 1,000 positive clips confirmed 97.8% +/- 0.9% labeling accuracy. Baseline experiments using MobileNetV3-Small achieved 99.57% +/- 0.25% accuracy and 0.9985 +/- 0.0002 AUC across three random seeds. SEABAD and the full curation pipeline are publicly released to support tropical BAD research and energy-efficient acoustic monitoring.

Muhammad Mun'im Ahmad Zabidi, Mohd Yamani Idna Idris, Norisma Idris

Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia

Training Data Curation Sound Event Detection Bioacoustics Passive Acoustic Monitoring Wildlife Monitoring

May 20, 2026cs.SD

A strongly annotated passive acoustic dataset for tropical bird monitoring

Passive acoustic monitoring enables continuous, non-invasive biodiversity assessment across diverse ecosystems. The scale of these datasets has driven the adoption of machine learning, with supervised approaches showing strong performance. However, supervised methods require time-resolved annotated datasets, which remain scarce, especially in complex tropical soundscapes. We present PteroSet, a curated dataset of strongly annotated Neotropical bird vocalizations recorded in Puerto Asis (Putumayo) and Pivijay (Magdalena), Colombia, between 2023 and 2025. The dataset comprises 563 recordings (73.62 h) and 15,372 time-frequency annotations, including 6,702 events identified to the species level across 168 species. We release the annotations in a COCO-inspired JSON schema that unifies audio files, taxonomic categories, and labels for machine learning workflows. Beyond providing annotated data, PteroSet serves as a realistic benchmark that highlights key characteristics of tropical soundscapes, including acoustic co-occurrence and domain shift across recording sites. We provide a deep learning baseline for binary bird detection, demonstrating PteroSet's usability and the challenges it presents.

Daniela Ruiz, Juan Sebastián Ulloa, Zhongqi Miao +11

Microsoft AI for Good Research Lab, Redmond, Washington, United States · Universidad de Los Andes, Bogotá, Colombia, Center for Research and Formation in Artificial Intelligence · Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia +2

Sound Event Detection Bioacoustics Passive Acoustic Monitoring Wildlife Monitoring

May 13, 2026cs.SD

Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study

Bioacoustic recognition requires fine-grained acoustic understanding to distinguish similar-sounding species. However, many large-scale data repositories such as iNaturalist are weakly annotated, often with only a single positive species label per recording, making supervised learning particularly challenging. Inspired by advances in computer vision, recent approaches have shifted toward self-supervised learning to capture the underlying structure of audio without relying on exhaustive annotations. In particular, masked autoencoders (MAE) have shown strong transferability on massive audio corpora, yet their effectiveness in more modest bioacoustic settings remains underexplored. In this work, we conduct a systematic study of MAE pretraining for species classification on iNatSounds, analyzing the impacts of pretraining data scale, domain specificity, data curation, and transfer strategies. Consistent with prior work, we find that models pretrained on diverse general audio data achieve the best transfer performance on iNatSounds. Contrary to observations from large-scale audio benchmarks, we find that (1) additional masked reconstruction pretraining on domain-specific data provides limited benefits and may even degrade performance relative to off-the-shelf models, and (2) selective data filtering offers a negligible advantage when the overall data scale is limited. Our results indicate that, in moderate-sized fine-grained bioacoustic settings, pretraining scale dominates objective design. These findings further clarify when MAE-based pretraining is effective and provide practical guidance for model selection under limited supervision.

Wuao Liu, Mustafa Chasmai, Subhransu Maji +1

University of Massachusetts Amherst

Masked Autoencoders Bioacoustics Self-Supervised Speech Representation Learning Audio Classification

May 11, 2026cs.SD

Multi-layer attentive probing improves transfer of audio representations for bioacoustics

Probing heads map the representations learned from audio by a machine learning model to downstream task labels and are a key component in evaluating representation learning. Most bioacoustic benchmarks use a fixed, low-capacity probe, such as a linear layer on the final encoder layer. While this standardization enables model comparisons, it may bias results by overlooking the interaction between encoder features and probe design. In this work, we systematically study different probing strategies across two bioacoustic benchmarks, BEANs and BirdSet. We evaluate last- and multi-layer probing, across linear and attention probes. We show that larger probe heads that leverage time information have superior performance. Our results suggest that current benchmarks may misrepresent encoder quality when relying on a last-layer probing setup. Multi-layer probing improves downstream task performance across all tested models, while attention probing has superior performance to linear probing for transformer models.

Marius Miron, David Robinson, Masato Hagiwara +15

Earth Species Project

Audio Representation Learning Bioacoustics Hidden-State Probing

May 8, 2026cs.SD

BeeVe: Unsupervised Acoustic State Discovery in Honey Bee Buzzing

Discovering structure in biological signals without supervision is a fundamental problem in computational intelligence, yet existing bioacoustic methods assume vocal production models or predefined semantic units, leaving non-vocal species poorly served. This work introduces BeeVe, an unsupervised framework for acoustic state discovery in collective honey bee buzzing. BeeVe uses the self-supervised Patchout Spectrogram Transformer (PaSST) as a frozen feature extractor, then trains a Vector-Quantized Variational Autoencoder (VQ-VAE) without labels on those embeddings, learning a finite discrete codebook of acoustic tokens directly from unlabelled hive audio. No labels, pretext tasks, or contrastive objectives are used at any stage. Post-hoc evaluation against known queen status reveals that the learned tokens separate queenright and queenless conditions with Jensen-Shannon Divergence values between 0.609 and 0.688, and that the queenless condition further decomposes into three internally coherent sub-states stable across experiments with different codebook sizes and random seeds. Token transition analysis confirms non-random sequential structure (p << 0.001) across all experiments. Generalisation to unseen recordings preserves both token overlap (Jaccard = 0.947) and global manifold topology. These results demonstrate that unsupervised discrete codebook learning can recover repeatable acoustic structure from a non-vocal biological signal without annotation, opening a path toward non-invasive acoustic hive health monitoring.

Hamze Hammami, Nidhal Abdulaziz

Audio Representation Learning Unsupervised Clustering Vector-Quantized Variational Autoencoder Bioacoustics

May 5, 2026cs.SD

Ecologically-Constrained Task Arithmetic for Multi-Taxa Bioacoustic Classifiers Without Shared Data

Training data for bioacoustics is scattered across taxa, regions, and institutions. Centralizing it all is often infeasible. We show that independently fine-tuned BEATs encoders can be composed into a unified 661-species classifier via task vector arithmetic without sharing data. We find that bioacoustic task vectors are near-orthogonal (cosine 0.01-0.09). Their separation aligns closely with spectral distribution distance, a gradient consistent with the acoustic niche hypothesis. This geometry makes simple averaging optimal while sign-conflict methods reduce accuracy by one to six percentage points. Composition also creates an asymmetric gap: species-rich groups lose accuracy relative to joint training while underrepresented taxa gain, a redistribution useful for equitable biodiversity monitoring. We verify linear mode connectivity across all taxonomic pairs, demonstrate zero-shot transfer to new regions, and identify domain negation as a boundary condition where composition fails. These results enable a collaborative paradigm for bioacoustics where institutions share only task vectors to assemble multi-taxa classifiers, preserving data privacy.

Ragib Amin Nihal, Benjamin Yen, Runwu Shi +2

Systems and Control Engineering, Institute of Science Tokyo, Japan · RIKEN BDR, Japan

Task Arithmetic Bioacoustics Model Merging Audio Classification

May 5, 2026cs.SD

Smart Passive Acoustic Monitoring: Embedding a Classifier on AudioMoth Microcontroller

Passive Acoustic Monitoring (PAM) is an efficient and non-invasive method for surveying ecosystems at a reduced cost. Typically, autonomous recorders allow the acquisition of vast bioacoustic datasets which are then analyzed. However, power consumption and data storage are both scarce and limit the duration of acquisition campaigns. To address this issue, we propose a smart PAM system which allows the in-situ analysis of the soundscape by embedding a classifier directly onto an AudioMoth microcontroller. Specifically, we propose an optimized yet simple 1D Convolutional Neural Network (1D-CNN) to classify the raw audio. The model focuses on the specific call of Scopoli Shearwater seabirds (endangered species) and is trained on a real-world dataset with a classification accuracy of 91% (balanced accuracy of 89%). We also propose a process to optimize the model to fit the severe resource constraints of the AudioMoth, achieving a ~10kB RAM memory footprint and 20ms inference time. Finally, we present an open-source tutorial of our model optimization and export strategy which can be used for embedding models beyond the scope of our study. Our modified version of the AudioMoth firmware adds two functions: (F1) which selectively records data when the target species has been detected and (F2) which logs the continuous classification results in real time. This work intends to facilitate the conception of intelligent sensors, enhancing the efficiency and scalability of bioacoustic monitoring campaigns.

Louis Lerbourg, Paul Peyret, Juliette Linossier +1

Univ. Grenoble Alpes, CEA, List, Grenoble, France · Biophonia, France

Efficient Neural Network Inference Sound Event Detection Edge ML Bioacoustics Passive Acoustic Monitoring Audio Classification+1

May 2, 2026cs.SD

BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations

Most work in audio enhancement targets human speech, while bioacoustics is less studied due to noisy recordings and the distinct traits of animal sounds. To fill this gap, we adapt speech enhancement methods and build BioSEN, a model made for bioacoustic signals. BioSEN has three modules: a multi-scale dual-axis attention unit for time-frequency feature extraction, a bio-harmonic multi-scale enhancement unit for capturing harmonic structures, and an energy-adaptive gating connection unit that uses frequency weights to keep vocalizations from being removed as noise. Tests on three bioacoustic datasets show that BioSEN matches or exceeds state-of-the-art speech enhancement models while using far less computation. These results show BioSEN's strength for bioacoustic audio enhancement and its promise for biodiversity monitoring and conservation.

Tianyu Song, Ton Viet Ta, Ngamta Thamwattana +2

Graduate School of Bioresource and Bioenvironmental Science, Kyushu University · Faculty of Agriculture, Kyushu University · School of Information and Physical Sciences, University of Newcastle +2

Apr 30, 2026eess.AS

From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings

We show that pretrained acoustic embeddings classify elephant vocalisations at a level approaching that of end-to-end supervised neural networks, without any fine-tuning of the embedding model. This result is of practical importance because annotated bioacoustic data are scarce and costly to obtain, leaving conventional supervised approaches prone to overfitting and to poor generalisation under domain shift. A broad range of embedding models drawn from general audio, speech, and bioacoustic domains is evaluated, all of which are either out-of-domain (containing no bioacoustic data) or out-of-species (containing no elephant call data). The embedding networks themselves remain fixed; only the lightweight downstream classifiers, which include a linear model and several small neural networks, are trained. Among the models considered, Perch 2.0 achieves the best cross-validated classification performance, attaining AUCs of 0.849 on African bush elephant (Loxodonta africana) calls and 0.936 on Asian elephant (Elephas maximus) calls, with Perch 1.0 close behind. The best-performing system is within 2.2 % of an end-to-end supervised elephant call classification system. A layerwise analysis of pretrained transformer encoders, considered as embedding models, shows that intermediate representations outperform final-layer outputs. The second layer of both wav2vec2.0 and HuBERT encodes sufficient information for effective elephant call classification; truncation at this layer therefore preserves classification performance whilst retaining only approximately 10 % of the parameters of the full network. Such compact embedding networks are well suited to on-device processing where computational resources are limited.

Christiaan M. Geldenhuys, Thomas R. Niesler

Department of Electrical and Electronic Engineering University of Stellenbosch

Audio Representation Learning Audio Understanding Bioacoustics Audio Classification

Apr 30, 2026cs.LG

Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification

Animals hear and vocalize across frequency ranges that differ substantially from humans, often extending into the ultrasonic domain. Yet most computational bioacoustics systems rely on audio models pre-trained at 16 kHz, restricting their usable bandwidth to the 0-8 kHz baseband and discarding higher-frequency information present in many bioacoustic recordings. We investigate a multi-band encoding framework that decomposes the full spectrum of animal calls into band features and fuses them into a unified representation. Similarity analyses on models show that certain encoders produce decorrelated band embeddings that improve class separation after fusion. Classification experiments on three bioacoustic datasets using eight pre-trained models and five fusion strategies show that fused representations consistently outperform the baseband and time-expansion baselines on two datasets, showing the potential of multi-band methods for full-spectrum encoding of animal calls.

Eklavya Sarkar, Marius Miron, David Robinson +6

Earth Species Project

Audio Representation Learning Frequency-Domain Feature Learning Bioacoustics Audio Classification

Aug 4, 2025eess.SP

Detecting and measuring respiratory events in horses during exercise with a microphone: deep learning vs. standard signal processing

Monitoring respiration parameters such as respiratory rate could be beneficial to understand the impact of training on equine health and performance and ultimately improve equine welfare. In this work, we compare deep learning-based methods to an adapted signal processing method to automatically detect cyclic respiratory events and extract the dynamic respiratory rate from microphone recordings during high intensity exercise in Standardbred trotters. Our deep learning models are able to detect exhalation sounds (median F1 score of 0.94) in noisy microphone signals and show promising results on unlabelled signals at lower exercising intensity, where the exhalation sounds are less recognisable. Temporal convolutional networks were better at detecting exhalation events and estimating dynamic respiratory rates (median F1: 0.94, Mean Absolute Error (MAE)

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Confidence Intervals (CI): 1.44

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1.04 bpm, Limits Of Agreements (LOA): 0.63

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7.06 bpm) than long short-term memory networks (median F1: 0.90, MAE

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CI: 3.11

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1.58 bpm) and signal processing methods (MAE

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CI: 2.36

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1.11 bpm). This work is the first to automatically detect equine respiratory sounds and automatically compute dynamic respiratory rates in exercising horses. In the future, our models will be validated on lower exercising intensity sounds and different microphone placements will be evaluated in order to find the best combination for regular monitoring.

Jeanne I. M. Parmentier, Rhana M. Aarts, Elin Hernlund +2

Utrecht University · University of Twente · Inertia Technology B.V +5

Temporal Convolutional Networks Sound Event Detection Bioacoustics Respiratory Sound Classification

Jul 18, 2025cs.SD

OpenBEATs: A Fully Open-Source General-Purpose Audio Encoder

Masked token prediction has emerged as a powerful pre-training objective across language, vision, and speech, offering the potential to unify these diverse modalities through a single pre-training task. However, its application for general audio understanding remains underexplored, with BEATs being the only notable example. BEATs has seen limited modifications due to the absence of open-source pre-training code. Furthermore, BEATs was trained only on AudioSet, restricting its broader downstream applicability. To address these gaps, we present OpenBEATs, an open-source framework that extends BEATs via multi-domain audio pre-training. We conduct comprehensive evaluations across six types of tasks, twenty five datasets, and three audio domains, including audio reasoning tasks such as audio question answering, entailment, and captioning. OpenBEATs achieves state-of-the-art performance on six bioacoustics datasets, two environmental sound datasets and five reasoning datasets, performing better than models exceeding a billion parameters at one-fourth their parameter size. These results demonstrate the effectiveness of multi-domain datasets and masked token prediction task to learn general-purpose audio representations. To promote further research and reproducibility, we release all pre-training and evaluation code, pretrained and fine-tuned checkpoints, and training logs at https://github.com/Shikhar-S/OpenBEATs

Shikhar Bharadwaj, Samuele Cornell, Kwanghee Choi +4

Carnegie Mellon University, USA · National Institute of Advanced Industrial Science and Technology (AIST), Japan

Audio Representation Learning Audio QA Audio Understanding Bioacoustics

Feb 19, 2025cs.SD

Localized time-frequency representation learning for bioacoustic classification in complex soundscapes

Prevailing bioacoustic classifiers assign species labels to fixed time-frequency windows rather than to individual vocalizations. When multiple vocalizations occur within the same window, predictions cannot be unambiguously linked to specific calls, which limits analyses at the level of individual vocalizations. This work introduces a framework for time-frequency localized bird classification. A Local-Context Classifier (LCC) identifies species from localized time-frequency events (TFEs), while a Dual-Context Classifier (DCC) combines local and global acoustic context through a fine-tuned bioacoustic foundation model. On an in-distribution dataset from Singapore comprising 306 vocalization classes from 103 bird species, the LCC achieves an F1-micro score of 79.3%, while combining local and global context through the DCC yields the highest overall performance (94.6%). To reduce labeled data requirements, the LCC is pre-trained via self-supervised contrastive learning, achieving an 18.8% relative gain on an out-of-distribution dataset. A focused evaluation on continuous soundscape recordings further demonstrates the potential of the framework for long-term monitoring applications. By preserving the time-frequency localization of individual vocalizations, the proposed framework supports both ecological monitoring and vocalization-level studies of animal acoustic behavior.

Simen Hexeberg, Mandar Chitre, Matthias Hoffmann-Kuhnt +1

ARL, Tropical Marine Science Institute, National University of Singapore · Department of Electrical & Computer Engineering, National University of Singapore · National Parks Board, Singapore

Bioacoustics Audio Classification

Oct 22, 2024cs.SD

Classifying bioacoustic data without individual call annotations using temporal convolutional networks and feature extractors

Bioacoustic data from Passive Acoustic Monitoring (PAM) generates large datasets where obtaining detailed auditing and labelling is often impractical, resulting in weak annotations (e.g., presence/absence of species over several minutes of recording). In order to effectively capture the complex temporal patterns and key features of long audio segments, we propose a framework comprising dataset standardisation, feature extraction, and classification via Temporal Convolutional Networks (TCN). This approach eliminates the necessity for setting heuristic decision rules or creating time-consuming strong labels. To demonstrate the effectiveness of our approach, we use sperm whale (\textit{Physeter macrocephalus}) click trains in 4-minute recordings as a case study, from a dataset comprising diverse sources and deployment conditions to maximise generalisability. Our TCN classifiers achieve recall rates exceeding 0.83 at a 0.13 false positive rate, comparable to agreement rates between expert annotators. We compare two methods of feature extraction, Variational AutoEncoders (VAEs) and traditional handpicking of features, and found them to yield similar performance results, with the VAE-based classifiers seeing a more stable performance across datasets and recording conditions. These results offer a way forward in leveraging numerous existing annotated bioacoustic datasets to train automatic classification models, effectively overcoming previous limitations associated with weak labels.

Laia Garrobé Fonollosa, Douglas Gillespie, Lina Stankovic +2

Sea Mammal Research Unit, School of Biology, University of St. Andrews, KY16 9TH, St. Andrews, Scotland · Electrical and Electronic Engineering, University of Strathclyde, Montrose Street, Glasgow, G1 1XJ, Scotland · Electrical and Electronic Engineering, University of Strathclyde, George Street, Glasgow, G1 1XW, Scotland

Bioacoustics Passive Acoustic Monitoring Audio Classification