Seeing Speech and Sound: Distinguishing and Locating Audios in Visual Scenes

• URL: http://arxiv.org/abs/2503.18880v1
• Date: Mon, 24 Mar 2025 16:56:04 GMT
• Title: Seeing Speech and Sound: Distinguishing and Locating Audios in Visual Scenes
• Authors: Hyeonggon Ryu, Seongyu Kim, Joon Son Chung, Arda Senocak,
• Abstract summary: We present a unified model capable of simultaneously grounding both spoken language and non-speech sounds within a visual scene.<n>Existing approaches are typically limited to handling either speech or non-speech sounds independently, or at best, together but sequentially without mixing.
• Score: 16.530816405275715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a unified model capable of simultaneously grounding both spoken language and non-speech sounds within a visual scene, addressing key limitations in current audio-visual grounding models. Existing approaches are typically limited to handling either speech or non-speech sounds independently, or at best, together but sequentially without mixing. This limitation prevents them from capturing the complexity of real-world audio sources that are often mixed. Our approach introduces a 'mix-and-separate' framework with audio-visual alignment objectives that jointly learn correspondence and disentanglement using mixed audio. Through these objectives, our model learns to produce distinct embeddings for each audio type, enabling effective disentanglement and grounding across mixed audio sources. Additionally, we created a new dataset to evaluate simultaneous grounding of mixed audio sources, demonstrating that our model outperforms prior methods. Our approach also achieves comparable or better performance in standard segmentation and cross-modal retrieval tasks, highlighting the benefits of our mix-and-separate approach.

Related papers

• Unleashing the Power of Natural Audio Featuring Multiple Sound Sources [54.38251699625379]
  Universal sound separation aims to extract clean audio tracks corresponding to distinct events from mixed audio. We propose ClearSep, a framework that employs a data engine to decompose complex naturally mixed audio into multiple independent tracks. In experiments, ClearSep achieves state-of-the-art performance across multiple sound separation tasks.
  arXiv  Detail & Related papers  (2025-04-24T17:58:21Z)
• 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)
• AudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codes [6.375996974877916]
  We propose a method named AudioFormer, which learns audio feature representations through the acquisition of discrete acoustic codes. Our research outcomes demonstrate that AudioFormer attains significantly improved performance compared to prevailing monomodal audio classification models.
  arXiv  Detail & Related papers  (2023-08-14T15:47:25Z)
• AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining [46.22290575167155]
  This paper proposes a framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework introduces a general representation of audio, called "language of audio" (LOA)
  arXiv  Detail & Related papers  (2023-08-10T17:55:13Z)
• Self-Supervised Visual Acoustic Matching [63.492168778869726]
  Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio. Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric.
  arXiv  Detail & Related papers  (2023-07-27T17:59:59Z)
• Language-Guided Audio-Visual Source Separation via Trimodal Consistency [64.0580750128049]
  A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform. We adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets.
  arXiv  Detail & Related papers  (2023-03-28T22:45:40Z)
• Joint Speech Recognition and Audio Captioning [37.205642807313545]
  Speech samples recorded in both indoor and outdoor environments are often contaminated with secondary audio sources. We aim to bring together the growing field of automated audio captioning (AAC) and the thoroughly studied automatic speech recognition (ASR) We propose several approaches for end-to-end joint modeling of ASR and AAC tasks.
  arXiv  Detail & Related papers  (2022-02-03T04:42:43Z)
• Self-Supervised Learning from Automatically Separated Sound Scenes [38.71803524843168]
  This paper explores the use of unsupervised automatic sound separation to decompose unlabeled sound scenes into semantically-linked views. We find that learning to associate input mixtures with their automatically separated outputs yields stronger representations than past approaches.
  arXiv  Detail & Related papers  (2021-05-05T15:37:17Z)
• VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency [111.55430893354769]
  Given a video, the goal is to extract the speech associated with a face in spite of simultaneous background sounds and/or other human speakers. Our approach jointly learns audio-visual speech separation and cross-modal speaker embeddings from unlabeled video. It yields state-of-the-art results on five benchmark datasets for audio-visual speech separation and enhancement.
  arXiv  Detail & Related papers  (2021-01-08T18:25:24Z)
• Audio-visual Speech Separation with Adversarially Disentangled Visual Representation [23.38624506211003]
  Speech separation aims to separate individual voice from an audio mixture of multiple simultaneous talkers. In our model, we use the face detector to detect the number of speakers in the scene and use visual information to avoid the permutation problem. Our proposed model is shown to outperform the state-of-the-art audio-only model and three audio-visual models.
  arXiv  Detail & Related papers  (2020-11-29T10:48:42Z)
• 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)

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.