Towards Attention-based Contrastive Learning for Audio Spoof Detection

• URL: http://arxiv.org/abs/2407.03514v1
• Date: Wed, 3 Jul 2024 21:25:12 GMT
• Title: Towards Attention-based Contrastive Learning for Audio Spoof Detection
• Authors: Chirag Goel, Surya Koppisetti, Ben Colman, Ali Shahriyari, Gaurav Bharaj,
• Abstract summary: Vision transformers (ViT) have made substantial progress for classification tasks in computer vision. We introduce ViTs for audio spoof detection task. We propose a novel attention-based contrastive learning framework (SSAST-CL) that uses cross-attention to aid the representation learning.
• Score: 3.08086566663567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision transformers (ViT) have made substantial progress for classification tasks in computer vision. Recently, Gong et. al. '21, introduced attention-based modeling for several audio tasks. However, relatively unexplored is the use of a ViT for audio spoof detection task. We bridge this gap and introduce ViTs for this task. A vanilla baseline built on fine-tuning the SSAST (Gong et. al. '22) audio ViT model achieves sub-optimal equal error rates (EERs). To improve performance, we propose a novel attention-based contrastive learning framework (SSAST-CL) that uses cross-attention to aid the representation learning. Experiments show that our framework successfully disentangles the bonafide and spoof classes and helps learn better classifiers for the task. With appropriate data augmentations policy, a model trained on our framework achieves competitive performance on the ASVSpoof 2021 challenge. We provide comparisons and ablation studies to justify our claim.

Related papers

• CPM: Class-conditional Prompting Machine for Audio-visual Segmentation [17.477225065057993]
  Class-conditional Prompting Machine (CPM) improves bipartite matching with a learning strategy combining class-agnostic queries with class-conditional queries. We conduct experiments on AVS benchmarks, demonstrating that our method achieves state-of-the-art (SOTA) segmentation accuracy.
  arXiv  Detail & Related papers  (2024-07-07T13:20:21Z)
• TalkNCE: Improving Active Speaker Detection with Talk-Aware Contrastive Learning [15.673602262069531]
  Active Speaker Detection (ASD) is a task to determine whether a person is speaking or not in a series of video frames. We propose TalkNCE, a novel talk-aware contrastive loss. Our method achieves state-of-the-art performances on AVA-ActiveSpeaker and ASW datasets.
  arXiv  Detail & Related papers  (2023-09-21T17:59:11Z)
• Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation [18.001730255429347]
  Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. We propose a new cost-effective strategy to build challenging and relatively unbiased high-quality audio-visual segmentation benchmarks. Experiments conducted on existing AVS datasets and on our new benchmark show that our method achieves state-of-the-art (SOTA) segmentation accuracy.
  arXiv  Detail & Related papers  (2023-04-06T09:54:06Z)
• Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning [60.501201259732625]
  We introduce task-adaptive saliency for EFCIL and propose a new framework, which we call Task-Adaptive Saliency Supervision (TASS) Our experiments demonstrate that our method can better preserve saliency maps across tasks and achieve state-of-the-art results on the CIFAR-100, Tiny-ImageNet, and ImageNet-Subset EFCIL benchmarks.
  arXiv  Detail & Related papers  (2022-12-16T02:43:52Z)
• Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection [88.74863771919445]
  We reveal the vulnerability of AVASD models under audio-only, visual-only, and audio-visual adversarial attacks. We also propose a novel audio-visual interaction loss (AVIL) for making attackers difficult to find feasible adversarial examples.
  arXiv  Detail & Related papers  (2022-10-03T08:10:12Z)
• Patch-level Representation Learning for Self-supervised Vision Transformers [68.8862419248863]
  Vision Transformers (ViTs) have gained much attention recently as a better architectural choice, often outperforming convolutional networks for various visual tasks. Inspired by this, we design a simple yet effective visual pretext task, coined SelfPatch, for learning better patch-level representations. We demonstrate that SelfPatch can significantly improve the performance of existing SSL methods for various visual tasks.
  arXiv  Detail & Related papers  (2022-06-16T08:01:19Z)
• AVATAR: Unconstrained Audiovisual Speech Recognition [75.17253531162608]
  We propose a new sequence-to-sequence AudioVisual ASR TrAnsformeR (AVATAR) trained end-to-end from spectrograms and full-frame RGB. We demonstrate the contribution of the visual modality on the How2 AV-ASR benchmark, especially in the presence of simulated noise. We also create a new, real-world test bed for AV-ASR called VisSpeech, which demonstrates the contribution of the visual modality under challenging audio conditions.
  arXiv  Detail & Related papers  (2022-06-15T17:33:19Z)
• Self-Promoted Supervision for Few-Shot Transformer [178.52948452353834]
  Self-promoted sUpervisioN (SUN) is a few-shot learning framework for vision transformers (ViTs) SUN pretrains the ViT on the few-shot learning dataset and then uses it to generate individual location-specific supervision for guiding each patch token. Experiments show that SUN using ViTs significantly surpasses other few-shot learning frameworks with ViTs and is the first one that achieves higher performance than those CNN state-of-the-arts.
  arXiv  Detail & Related papers  (2022-03-14T12:53:27Z)
• MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound [90.1857707251566]
  We introduce MERLOT Reserve, a model that represents videos jointly over time. We replace snippets of text and audio with a MASK token; the model learns by choosing the correct masked-out snippet. Our objective learns faster than alternatives, and performs well at scale.
  arXiv  Detail & Related papers  (2022-01-07T19:00:21Z)
• SSAST: Self-Supervised Audio Spectrogram Transformer [19.09439093130855]
  We propose to pretrain the Audio Spectrogram Transformer (AST) model with joint discriminative and generative masked spectrogram patch modeling (MSPM) using unlabeled audio. We evaluate our pretrained models on both audio and speech classification tasks including audio event classification, keyword spotting, emotion recognition, and speaker identification. To the best of our knowledge, it is the first patch-based self-supervised learning framework in the audio and speech domain, and also the first self-supervised learning framework for AST.
  arXiv  Detail & Related papers  (2021-10-19T07:58:28Z)
• How to Teach DNNs to Pay Attention to the Visual Modality in Speech Recognition [10.74796391075403]
  This study investigates the inner workings of AV Align and visualises the audio-visual alignment patterns. We find that AV Align learns to align acoustic and visual representations of speech at the frame level on TCD-TIMIT in a generally monotonic pattern. We propose a regularisation method which involves predicting lip-related Action Units from visual representations.
  arXiv  Detail & Related papers  (2020-04-17T13:59:19Z)

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.