Unsupervised Sound Localization via Iterative Contrastive Learning

• URL: http://arxiv.org/abs/2104.00315v1
• Date: Thu, 1 Apr 2021 07:48:29 GMT
• Title: Unsupervised Sound Localization via Iterative Contrastive Learning
• Authors: Yan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee, Yen-Yu Lin, Ming-Hsuan Yang
• Abstract summary: We propose an iterative contrastive learning framework that requires no data annotations. We then use the pseudo-labels to learn the correlation between the visual and audio signals sampled from the same video. Our iterative strategy gradually encourages the localization of the sounding objects and reduces the correlation between the non-sounding regions and the reference audio.
• Score: 106.56167882750792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sound localization aims to find the source of the audio signal in the visual scene. However, it is labor-intensive to annotate the correlations between the signals sampled from the audio and visual modalities, thus making it difficult to supervise the learning of a machine for this task. In this work, we propose an iterative contrastive learning framework that requires no data annotations. At each iteration, the proposed method takes the 1) localization results in images predicted in the previous iteration, and 2) semantic relationships inferred from the audio signals as the pseudo-labels. We then use the pseudo-labels to learn the correlation between the visual and audio signals sampled from the same video (intra-frame sampling) as well as the association between those extracted across videos (inter-frame relation). Our iterative strategy gradually encourages the localization of the sounding objects and reduces the correlation between the non-sounding regions and the reference audio. Quantitative and qualitative experimental results demonstrate that the proposed framework performs favorably against existing unsupervised and weakly-supervised methods on the sound localization task.

Related papers

• Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval [7.459223771397159]
  Cross-modal data (e.g. audiovisual) have different distributions and representations that cannot be directly compared. To bridge the gap between audiovisual modalities, we learn a common subspace for them by utilizing the intrinsic correlation in the natural synchronization of audio-visual data with the aid of annotated labels. We propose a new AV-CMR model to optimize semantic features by directly predicting labels and then measuring the intrinsic correlation between audio-visual data using complete cross-triple loss.
  arXiv  Detail & Related papers  (2022-11-07T10:37:14Z)
• MarginNCE: Robust Sound Localization with a Negative Margin [23.908770938403503]
  The goal of this work is to localize sound sources in visual scenes with a self-supervised approach. We show that using a less strict decision boundary in contrastive learning can alleviate the effect of noisy correspondences in sound source localization.
  arXiv  Detail & Related papers  (2022-11-03T16:44:14Z)
• Visual Scene Graphs for Audio Source Separation [65.47212419514761]
  State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. We propose Audio Visual Scene Graph Segmenter (AVSGS), a novel deep learning model that embeds the visual structure of the scene as a graph and segments this graph into subgraphs. Our pipeline is trained end-to-end via a self-supervised task consisting of separating audio sources using the visual graph from artificially mixed sounds.
  arXiv  Detail & Related papers  (2021-09-24T13:40:51Z)
• Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning [141.38505371646482]
  Cross-modal correlation provides an inherent supervision for video unsupervised representation learning. This paper introduces a pretext task, Cross-Modal Attention Consistency (CMAC), for exploring the bidirectional local correspondence property. CMAC aims to align the regional attention generated purely from the visual signal with the target attention generated under the guidance of acoustic signal.
  arXiv  Detail & Related papers  (2021-06-13T07:41:15Z)
• Positive Sample Propagation along the Audio-Visual Event Line [29.25572713908162]
  Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs) We propose a new positive sample propagation (PSP) module to discover and exploit closely related audio-visual pairs. We perform extensive experiments on the public AVE dataset and achieve new state-of-the-art accuracy in both fully and weakly supervised settings.
  arXiv  Detail & Related papers  (2021-04-01T03:53:57Z)
• Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation [73.1652905564163]
  We address the problem of separating individual speech signals from videos using audio-visual neural processing. Most conventional approaches utilize frame-wise matching criteria to extract shared information between co-occurring audio and video. We propose a cross-modal affinity network (CaffNet) that learns global correspondence as well as locally-varying affinities between audio and visual streams.
  arXiv  Detail & Related papers  (2021-03-25T15:39:12Z)
• Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization [113.19483349876668]
  This paper proposes a novel approach to sound event localization by utilizing an attention-based sequence-to-sequence model. It yields superior localization performance compared to state-of-the-art methods in both anechoic and reverberant conditions.
  arXiv  Detail & Related papers  (2021-02-28T07:52:20Z)
• Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain [103.3388198420822]
  Esting the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization. This paper proposes a novel audiovisual data fusion framework for speaker localization by assigning individual dynamic stream weights to specific regions. A performance evaluation using audiovisual recordings yields promising results, with the proposed fusion approach outperforming all baseline models.
  arXiv  Detail & Related papers  (2021-02-23T09:59:31Z)
• Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning [17.6311804187027]
  An underlying correlation between audio and visual events can be utilized as free supervised information to train a neural network. We propose a novel self-supervised framework with co-attention mechanism to learn generic cross-modal representations from unlabelled videos. Experiments show that our model achieves state-of-the-art performance on the pretext task while having fewer parameters compared with existing methods.
  arXiv  Detail & Related papers  (2020-08-13T10:08:12Z)

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.