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연구실 공부

[논문] T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis https://arxiv.org/abs/2401.09294 T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound SynthesisFoley sound, audio content inserted synchronously with videos, plays a critical role in the user experience of multimedia content. Recently, there has been active research in Foley sound synthesis, leveraging the advancements in deep generative models. Howarxiv... 연구실 공부 2024. 8. 9.
[논문] Contrastive Audio-Visual Masked Autoencoder https://arxiv.org/abs/2210.07839 Contrastive Audio-Visual Masked AutoencoderIn this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masarxiv.org해당 논문을 보고 작성했습니다. Abstract이 논문에서 저자들은 single modality에서 동.. 연구실 공부 2024. 8. 8.
[논문] High-Resolution Image Synthesis with Latent Diffusion Models https://arxiv.org/abs/2112.10752 High-Resolution Image Synthesis with Latent Diffusion ModelsBy decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism tarxiv.org해당 논문을 보고 작성했습니다. Abstractimage를 생성하는 pr.. 연구실 공부 2024. 8. 6.
[논문] Using Cyclic Noise as the Source Signal for Neural Source-Filter-based Speech Waveform Model https://www.isca-archive.org/interspeech_2020/wang20u_interspeech.html ISCA ArchiveUsing Cyclic Noise as the Source Signal for Neural Source-Filter-Based Speech Waveform Model Xin Wang, Junichi Yamagishi Neural source-filter (NSF) waveform models generate speech waveforms by morphing sine-based source signals through dilated convolutionwww.isca-archive.org해당 논문을 보고 작성했습니다. AbstractNeural source-.. 연구실 공부 2024. 8. 5.
[논문] Neural Source-Filter-Based Waveform Model for Statistical Parameteric Speech Synthesis https://ieeexplore.ieee.org/document/8682298 Neural Source-filter-based Waveform Model for Statistical Parametric Speech SynthesisNeural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure. Although faster non-AR models were recently reported, theieeexplore.i.. 연구실 공부 2024. 8. 4.
[논문] CREPE: A Convolutional Representation for Pitch Estimation https://arxiv.org/abs/1802.06182 CREPE: A Convolutional Representation for Pitch EstimationThe task of estimating the fundamental frequency of a monophonic sound recording, also known as pitch tracking, is fundamental to audio processing with multiple applications in speech processing and music information retrieval. To date, the best performingarxiv.org해당 논문을 보고 작성했습니다. Abstractpitch tracking이라.. 연구실 공부 2024. 8. 2.
[논문] DIFF-FOLEY: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models https://arxiv.org/abs/2306.17203 Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion ModelsThe Video-to-Audio (V2A) model has recently gained attention for its practical application in generating audio directly from silent videos, particularly in video/film production. However, previous methods in V2A have limited generation quality in terms ofarxiv.org해당 논문을 보고 작성했습니다. Abstr.. 연구실 공부 2024. 8. 2.
[논문] Text-to-Audio Generation Synchronized with Videos https://arxiv.org/abs/2403.07938 Text-to-Audio Generation Synchronized with VideosIn recent times, the focus on text-to-audio (TTA) generation has intensified, as researchers strive to synthesize audio from textual descriptions. However, most existing methods, though leveraging latent diffusion models to learn the correlation between auarxiv.org위 논문을 보고 작성했습니다. Abstract최근 text-to-audio (TTA) gen.. 연구실 공부 2024. 7. 29.
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