Perch 2.0 transfers 'whale' to underwater tasks

• URL: http://arxiv.org/abs/2512.03219v1
• Date: Tue, 02 Dec 2025 20:49:41 GMT
• Title: Perch 2.0 transfers 'whale' to underwater tasks
• Abstract summary: Perch 2.0 is a supervised bioacoustics foundation model pretrained on 14,597 species.<n>Given that Perch 2.0 includes almost no marine mammal audio or classes in the training data, we evaluate Perch 2.0 performance on marine mammal and underwater audio tasks.
• Score: 6.216539628488371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Perch 2.0 is a supervised bioacoustics foundation model pretrained on 14,597 species, including birds, mammals, amphibians, and insects, and has state-of-the-art performance on multiple benchmarks. Given that Perch 2.0 includes almost no marine mammal audio or classes in the training data, we evaluate Perch 2.0 performance on marine mammal and underwater audio tasks through few-shot transfer learning. We perform linear probing with the embeddings generated from this foundation model and compare performance to other pretrained bioacoustics models. In particular, we compare Perch 2.0 with previous multispecies whale, Perch 1.0, SurfPerch, AVES-bio, BirdAVES, and Birdnet V2.3 models, which have open-source tools for transfer-learning and agile modeling. We show that the embeddings from the Perch 2.0 model have consistently high performance for few-shot transfer learning, generally outperforming alternative embedding models on the majority of tasks, and thus is recommended when developing new linear classifiers for marine mammal classification with few labeled examples.

Related papers

• Moirai 2.0: When Less Is More for Time Series Forecasting [91.36760228926214]
  Moirai 2.0 is a decoder-only foundation model trained on a new corpus of 36M series.<n>It ranks among the top pretrained models while achieving a strong trade-off between accuracy, speed, and model size.<n>In terms of efficiency and model size, Moirai 2.0 is twice as fast and thirty times smaller than its prior best version, Moirai 1.0-Large.
  arXiv  Detail & Related papers  (2025-11-12T12:15:35Z)
• FishAI 2.0: Marine Fish Image Classification with Multi-modal Few-shot Learning [4.649981516403062]
  FishAI 2.0 integrates multimodal few-shot deep learning techniques with image generation for data augmentation.<n>FishAI 2.0 achieves a Top-1 accuracy of 91.67 percent and Top-5 accuracy of 97.97 percent at the family level.
  arXiv  Detail & Related papers  (2025-09-26T20:54:35Z)
• gpt-oss-120b & gpt-oss-20b Model Card [128.07668730833967]
  We present two open-weight reasoning models that push the frontier of accuracy and inference cost.<n>The models use an efficient mixture-of-expert transformer architecture and are trained using large-scale distillation and reinforcement learning.
  arXiv  Detail & Related papers  (2025-08-08T19:24:38Z)
• Perch 2.0: The Bittern Lesson for Bioacoustics [7.540404494602192]
  Perch is a performant pre-trained model for bioacoustics.<n>It was trained in supervised fashion, providing both off-the-shelf classification scores for thousands of vocalizing species as well as strong embeddings for transfer learning.<n>In this new release, Perch 2.0, we expand from training exclusively on avian species to a large multi-taxa dataset.
  arXiv  Detail & Related papers  (2025-08-06T17:34:43Z)
• Foundation Models for Bioacoustics -- a Comparative Review [0.9109149174920012]
  We review bioacoustic foundation models by thoroughly analysing design decisions such as model architecture, pretraining scheme, and training paradigm.<n>We evaluate selected foundation models on classification tasks from the BEANS and BirdSet benchmarks.<n>Our comprehensive experimental analysis reveals that BirdMAE, trained on large-scale bird song data with a self-supervised objective, achieves the best performance on the BirdSet benchmark.
  arXiv  Detail & Related papers  (2025-08-02T09:15:16Z)
• Qwen2.5 Technical Report [122.13958993185952]
  We introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs.<n>Compared to previous iterations, Qwen 2.5 has been significantly improved during both the pre-training and post-training stages.<n>Open-weight offerings include base and instruction-tuned models, with quantized versions available.<n>For hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2.5-Turbo and Qwen2.5-Plus.
  arXiv  Detail & Related papers  (2024-12-19T17:56:09Z)
• Benchmarking Large Language Models for Image Classification of Marine Mammals [4.274291455715579]
  We build a benchmark dataset with 1,423 images of 65 kinds of marine mammals. Each animal is uniquely classified into different levels of class, ranging from species-level to medium-level to group-level. We evaluate several approaches for classifying these marine mammals.
  arXiv  Detail & Related papers  (2024-10-22T01:49:49Z)
• AudioProtoPNet: An interpretable deep learning model for bird sound classification [1.49199020343864]
  This study introduces AudioProtoPNet, an adaptation of the Prototypical Part Network (ProtoPNet) for multi-label bird sound classification. It is an inherently interpretable model that uses a ConvNeXt backbone to extract embeddings. The model was trained on the BirdSet training dataset, which consists of 9,734 bird species and over 6,800 hours of recordings.
  arXiv  Detail & Related papers  (2024-04-16T09:37:41Z)
• Exploring Meta Information for Audio-based Zero-shot Bird Classification [113.17261694996051]
  This study investigates how meta-information can improve zero-shot audio classification. We use bird species as an example case study due to the availability of rich and diverse meta-data.
  arXiv  Detail & Related papers  (2023-09-15T13:50:16Z)
• LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech [70.3307853082527]
  This work introduces LeBenchmark 2.0 an open-source framework for assessing and building SSL-equipped French speech technologies. It includes documented, large-scale and heterogeneous corpora with up to 14,000 hours of heterogeneous speech. It includes ten pre-trained SSL wav2vec 2.0 models containing from 26 million to one billion learnable parameters shared with the community.
  arXiv  Detail & Related papers  (2023-09-11T14:13:09Z)
• TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos [63.85815474157357]
  We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
  arXiv  Detail & Related papers  (2022-11-17T23:55:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.