Learning Self-Supervised Audio-Visual Representations for Sound Recommendations

• URL: http://arxiv.org/abs/2412.07406v1
• Date: Tue, 10 Dec 2024 10:56:02 GMT
• Title: Learning Self-Supervised Audio-Visual Representations for Sound Recommendations
• Authors: Sudha Krishnamurthy,
• Abstract summary: We propose a novel self-supervised approach for learning audio and visual representations from unlabeled videos. The approach uses an attention mechanism to learn the relative importance of convolutional features extracted at different resolutions from the audio and visual streams. We evaluate the representations learned by the model to classify audio-visual correlation as well as to recommend sound effects for visual scenes.
• Score: 0.0
• License:
• Abstract: We propose a novel self-supervised approach for learning audio and visual representations from unlabeled videos, based on their correspondence. The approach uses an attention mechanism to learn the relative importance of convolutional features extracted at different resolutions from the audio and visual streams and uses the attention features to encode the audio and visual input based on their correspondence. We evaluated the representations learned by the model to classify audio-visual correlation as well as to recommend sound effects for visual scenes. Our results show that the representations generated by the attention model improves the correlation accuracy compared to the baseline, by 18% and the recommendation accuracy by 10% for VGG-Sound, which is a public video dataset. Additionally, audio-visual representations learned by training the attention model with cross-modal contrastive learning further improves the recommendation performance, based on our evaluation using VGG-Sound and a more challenging dataset consisting of gameplay video recordings.

Related papers

• Robust Audiovisual Speech Recognition Models with Mixture-of-Experts [67.75334989582709]
  We introduce EVA, leveraging the mixture-of-Experts for audioVisual ASR to perform robust speech recognition for in-the-wild'' videos. We first encode visual information into visual tokens sequence and map them into speech space by a lightweight projection. Experiments show our model achieves state-of-the-art results on three benchmarks.
  arXiv  Detail & Related papers  (2024-09-19T00:08:28Z)
• AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection [2.985620880452743]
  We present Audio-Visual Feature Fusion (AVFF), a two-stage cross-modal learning method for improved deepfake detection. To extract rich cross-modal representations, we use contrastive learning and autoencoding objectives, and introduce a novel audio-visual masking and feature fusion strategy. We report 98.6% accuracy and 99.1% AUC on the FakeAVCeleb dataset, outperforming the current audio-visual state-of-the-art by 14.9% and 9.9%, respectively.
  arXiv  Detail & Related papers  (2024-06-05T05:20:12Z)
• AdVerb: Visually Guided Audio Dereverberation [49.958724234969445]
  We present AdVerb, a novel audio-visual dereverberation framework. It uses visual cues in addition to the reverberant sound to estimate clean audio.
  arXiv  Detail & Related papers  (2023-08-23T18:20:59Z)
• 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)
• Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment [22.912401512161132]
  We design a model that works by scheduling the learning procedure of each model component to associate audio-visual modalities. We translate the input audio to visual features, then use a pre-trained generator to produce an image. We obtain substantially better results on the VEGAS and VGGSound datasets than prior approaches.
  arXiv  Detail & Related papers  (2023-03-30T16:01:50Z)
• AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR [79.21857972093332]
  We present AVFormer, a method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively.
  arXiv  Detail & Related papers  (2023-03-29T07:24:28Z)
• Role of Audio in Audio-Visual Video Summarization [8.785359786012302]
  We propose a new audio-visual video summarization framework integrating four ways of audio-visual information fusion with GRU-based and attention-based networks. Experimental evaluations on the TVSum dataset attain F1 score and Kendall-tau score improvements for the audio-visual video summarization.
  arXiv  Detail & Related papers  (2022-12-02T09:11:49Z)
• Self-supervised Contrastive Learning for Audio-Visual Action Recognition [7.188231323934023]
  The underlying correlation between audio and visual modalities can be utilized to learn supervised information for unlabeled videos. We propose an end-to-end self-supervised framework named Audio-Visual Contrastive Learning (A), to learn discriminative audio-visual representations for action recognition.
  arXiv  Detail & Related papers  (2022-04-28T10:01:36Z)
• Audiovisual Highlight Detection in Videos [78.26206014711552]
  We present results from two experiments: efficacy study of single features on the task, and an ablation study where we leave one feature out at a time. For the video summarization task, our results indicate that the visual features carry most information, and including audiovisual features improves over visual-only information. Results indicate that we can transfer knowledge from the video summarization task to a model trained specifically for the task of highlight detection.
  arXiv  Detail & Related papers  (2021-02-11T02:24:00Z)
• Self-Supervised Learning of Audio-Visual Objects from Video [108.77341357556668]
  We introduce a model that uses attention to localize and group sound sources, and optical flow to aggregate information over time. We demonstrate the effectiveness of the audio-visual object embeddings that our model learns by using them for four downstream speech-oriented tasks.
  arXiv  Detail & Related papers  (2020-08-10T16:18:01Z)
• Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision [63.564385139097624]
  We propose a method to learn self-supervised speech representations from the raw audio waveform. We train a raw audio encoder by combining audio-only self-supervision (by predicting informative audio attributes) with visual self-supervision (by generating talking faces from audio) Our results demonstrate the potential of multimodal self-supervision in audiovisual speech for learning good audio representations.
  arXiv  Detail & Related papers  (2020-07-08T14:07:06Z)

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.