Related papers: A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

URL: http://arxiv.org/abs/2608.14824v1
Date: Fri, 14 Aug 2026 18:54:24 GMT
Title: A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification
Abstract summary: parameter-free evaluation of nearest-centroid classification for elephant vocalisations on fixed pretrained acoustic embeddings.<n>We present a parameter-free evaluation of nearest-centroid classification for elephant vocalisations on fixed pretrained acoustic embeddings.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: We present a parameter-free episodic evaluation of nearest-centroid classification for elephant vocalisations on fixed pretrained acoustic embeddings, across the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets. Rather than asking which embedding yields the best classifier when trained on all available labelled data, we ask how the simplest classifier performs as labelled exemplars per class are varied. Each class is represented by the mean of its support-set embeddings, and each query is assigned to the nearest centroid under squared Euclidean distance. We evaluate this centroid classifier on the Perch (ver. 1), Perch (ver. 2), and HuBERT (base, layer 2) embeddings, together with mel frequency cepstral coefficient (MFCC) features, in an N-way k-shot manner under the same cross-validation protocol as the trained baselines. A bootstrap over 100 resampled support sets quantifies the sampling noise. On the smaller, low-resource EV dataset, the centroid classifier using the stronger Perch (ver. 1) and Perch (ver. 2) embeddings overtakes the fully-trained logistic regression classifier from a single exemplar per class and the stronger recurrent classifier from two. Over the reduced set of call types on which the strongly-supervised end-to-end baseline was trained, the centroid classifier matches and then surpasses that baseline in mean average precision (mAP), from a few exemplars per class. On the larger LDC dataset, where labelled exemplars are abundant, the trained baselines retain their advantage at every k considered. At five exemplars per class, the centroid classifier using the strongest embedding, Perch (ver. 2), attains a mAP of 0.542 on the EV dataset and 0.368 on the LDC dataset. Parameter-free nearest-centroid classification is the stronger choice when labelled exemplars are few and the fixed embedding already encodes the features that separate the call types.

Related papers

Ensemble of classifiers for speech evaluation [0.0]
The article describes an attempt to apply an ensemble of binary classifiers to solve the problem of speech assessment in medicine.<n>A dataset was compiled based on quantitative and expert assessments of syllable pronunciation quality.
arXiv Detail & Related papers (2024-12-29T17:28:32Z)
Co-training for Low Resource Scientific Natural Language Inference [65.37685198688538]
We propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels. By assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines.
arXiv Detail & Related papers (2024-06-20T18:35:47Z)
Deep Imbalanced Regression via Hierarchical Classification Adjustment [50.19438850112964]
Regression tasks in computer vision are often formulated into classification by quantizing the target space into classes. The majority of training samples lie in a head range of target values, while a minority of samples span a usually larger tail range. We propose to construct hierarchical classifiers for solving imbalanced regression tasks. Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks.
arXiv Detail & Related papers (2023-10-26T04:54:39Z)
The Fixed Sub-Center: A Better Way to Capture Data Complexity [1.583842747998493]
We propose to use Fixed Sub-Center (F-SC) to create more discrepant sub-centers. The experimental results show that F-SC significantly improves the accuracy of both image classification and fine-grained recognition tasks.
arXiv Detail & Related papers (2022-03-24T08:21:28Z)
A Topological Data Analysis Based Classifier [1.6668132748773563]
This paper proposes an algorithm that applies Topological Data Analysis directly to multi-class classification problems. The proposed algorithm builds a filtered simplicial complex on the dataset. On average, the proposed TDABC method was better than KNN and weighted-KNN.
arXiv Detail & Related papers (2021-11-09T15:54:16Z)
Prototypical Classifier for Robust Class-Imbalanced Learning [64.96088324684683]
We propose textitPrototypical, which does not require fitting additional parameters given the embedding network. Prototypical produces balanced and comparable predictions for all classes even though the training set is class-imbalanced. We test our method on CIFAR-10LT, CIFAR-100LT and Webvision datasets, observing that Prototypical obtains substaintial improvements compared with state of the arts.
arXiv Detail & Related papers (2021-10-22T01:55:01Z)
Does Adversarial Oversampling Help us? [10.210871872870737]
We propose a three-player adversarial game-based end-to-end method to handle class imbalance in datasets. Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach. The effectiveness of our proposed method has been validated with high-dimensional, highly imbalanced and large-scale multi-class datasets.
arXiv Detail & Related papers (2021-08-20T05:43:17Z)
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
arXiv Detail & Related papers (2021-06-09T12:02:29Z)
PLM: Partial Label Masking for Imbalanced Multi-label Classification [59.68444804243782]
Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. We propose a method, Partial Label Masking (PLM), which utilizes this ratio during training. Our method achieves strong performance when compared to existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label (imbalanced CIFAR-10 and CIFAR-100) image classification datasets.
arXiv Detail & Related papers (2021-05-22T18:07:56Z)
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification [94.55805516167369]
We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
arXiv Detail & Related papers (2021-02-01T07:36:38Z)
Predicting Classification Accuracy When Adding New Unobserved Classes [8.325327265120283]
We study how a classifier's performance can be used to extrapolate its expected accuracy on a larger, unobserved set of classes. We formulate a robust neural-network-based algorithm, "CleaneX", which learns to estimate the accuracy of such classifiers on arbitrarily large sets of classes.
arXiv Detail & Related papers (2020-10-28T14:37:25Z)

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