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연구실 공부

[논문] CLAP: Learning Audio Concepts from Natural Language Supervision https://arxiv.org/abs/2206.04769 CLAP: Learning Audio Concepts From Natural Language SupervisionMainstream Audio Analytics models are trained to learn under the paradigm of one class label to many recordings focusing on one task. Learning under such restricted supervision limits the flexibility of models because they require labeled audio for traininarxiv.org해당 논문을 보고 작성했습니다. Abstract주류 오디오 분석 m.. 연구실 공부 2024. 6. 27.
[논문] Soundify: Matching Sound Effect to Video https://arxiv.org/abs/2112.09726 Soundify: Matching Sound Effects to VideoIn the art of video editing, sound helps add character to an object and immerse the viewer within a space. Through formative interviews with professional editors (N=10), we found that the task of adding sounds to video can be challenging. This paper presenarxiv.org해당 논문을 보고 작성했습니다. Abstractvideo 편집 분야에서, 소리는 물체의 특성을 추가하거나 .. 연구실 공부 2024. 6. 24.
[논문] AudioCLIP: Extending CLIP to Image, Text and Audio https://arxiv.org/abs/2106.13043 AudioCLIP: Extending CLIP to Image, Text and AudioIn the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-specific tasks and approaches together, which provides the community with new oarxiv.org해당 논문을 보고 작성했습니다. Abstractdomain specific task와 ap.. 연구실 공부 2024. 6. 23.
[논문] AudioLDM: Text-to-Audio Generation with Latent Diffusion Models https://arxiv.org/abs/2301.12503 AudioLDM: Text-to-Audio Generation with Latent Diffusion ModelsText-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose Audioarxiv.org해당 논문을 보고 작성했습니다. AbstractText-to-aud.. 연구실 공부 2024. 6. 22.
[논문] Generating Realistic Images from In-the-wild Sounds https://arxiv.org/abs/2309.02405 Generating Realistic Images from In-the-wild SoundsRepresenting wild sounds as images is an important but challenging task due to the lack of paired datasets between sound and images and the significant differences in the characteristics of these two modalities. Previous studies have focused on generatingarxiv.org해당 논문을 보고 작성했습니다. Abstractimage로 wild sound를 나타내는 .. 연구실 공부 2024. 6. 20.
[논문] What Do I Hear? Generating Sounds for Visual with ChatGPT https://arxiv.org/abs/2311.05609 What Do I Hear? Generating Sounds for Visuals with ChatGPTThis short paper introduces a workflow for generating realistic soundscapes for visual media. In contrast to prior work, which primarily focus on matching sounds for on-screen visuals, our approach extends to suggesting sounds that may not be immediately varxiv.org해당 논문을 보고 작성했습니다. Introduction이 논문은 visual.. 연구실 공부 2024. 6. 19.
[논문] VoiceLDM: Text-to-Speech with Environmental Context https://arxiv.org/abs/2309.13664 VoiceLDM: Text-to-Speech with Environmental ContextThis paper presents VoiceLDM, a model designed to produce audio that accurately follows two distinct natural language text prompts: the description prompt and the content prompt. The former provides information about the overall environmental context of tharxiv.org해당 논문을 보고 작성했습니다. Abstract이 논문은 VoiceLDM이라는 두 nat.. 연구실 공부 2024. 6. 19.
[논문] Environment Aware Text-to-Speech Synthesis https://arxiv.org/abs/2110.03887 Environment Aware Text-to-Speech SynthesisThis study aims at designing an environment-aware text-to-speech (TTS) system that can generate speech to suit specific acoustic environments. It is also motivated by the desire to leverage massive data of speech audio from heterogeneous sources in TTS sysarxiv.org해당 논문을 보고 작성했습니다. Abstract이 연구는 acoustic environment에 맞는 s.. 연구실 공부 2024. 6. 18.
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