arXiv:2607. 14474v1 Announce Type: cross Abstract: This paper details the DS@GT ARC team's approach to BirdCLEF+ 2026, multi-label detection of animal vocalizations in soundscapes from the Pantanal wetlands.
arXiv:2609.15221v1 Announce Type: cross Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-spec...
arXiv:2606. 14662v1 Announce Type: new Abstract: Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task.
arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.
arXiv:2512.07571v3 Announce Type: replace Abstract: This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tune...
arXiv:2607. 03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets.
The paper presents a lightweight ResNet-based two-stage cascade for passive acoustic monitoring of killer whales. First, it detects vocalizations, then it classifies confident detections into five eastern North Pacific ecotypes, abstaining on ambiguous calls. The pipeline achieves high macro‑F1 scores on the DCLDE 2027 dataset and improves real‑time inference speed, while active learning adapts the detector to new acoustic environments.
The paper presents a compact underwater acoustic classification framework that integrates multi-representation feature engineering, temporal statistical pooling, and lightweight convolutional architectures for acoustic time-frequency and cochlear representations. Experiments on the ShipsEar dataset show a two-layer CNN achieving a macro F1 of 0.9918 and an RBF-SVM reaching 0.9883, but recording provenance issues limit verification of generalisation. When evaluated on the DeepShip dataset with recording-level partitioning, a 157K-parameter CNN attains a macro F1 of 0.7226, while a larger ResNet18 does not improve validation performance, underscoring the need for representation-aware design and rigorous evaluation for deployable systems.
arXiv:2606. 13236v1 Announce Type: cross Abstract: Passive acoustic monitoring holds great promise for ecological inference, yet existing automated tools are typically narrowly trained and non-transferable.
arXiv:2606. 02631v1 Announce Type: cross Abstract: This paper studies whether audio, images, and video can share a common wavelet token schema rather than relying on separate modality-specific latent grids.
AudioFuse is a hybrid architecture that jointly learns from spectrograms and raw waveforms to classify phonocardiograms. It combines a wide-and-shallow Vision Transformer for spectral features with a shallow 1D CNN for temporal waveforms, reducing overfitting while capturing complementary information. On the PhysioNet 2016 dataset, AudioFuse achieves a state‑of‑the‑art ROC‑AUC of 0.8608 and shows superior robustness to domain shift on the PASCAL dataset, outperforming both spectrogram‑only and waveform‑only baselines.
arXiv:2607. 14072v1 Announce Type: new Abstract: Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data.
arXiv:2606. 19039v1 Announce Type: cross Abstract: The mismatch between continuous acoustic signals and discrete event-driven processing remains a fundamental bottleneck for neuromorphic speech processing.