no code implementations • 28 May 2024 • Koichi Saito, Dongjun Kim, Takashi Shibuya, Chieh-Hsin Lai, Zhi Zhong, Yuhta Takida, Yuki Mitsufuji
Furthermore, we demonstrate SoundCTM's capability of controllable sound generation in a training-free manner.
1 code implementation • 28 May 2024 • Yixiao Zhang, Yukara Ikemiya, Woosung Choi, Naoki Murata, Marco A. Martínez-Ramírez, Liwei Lin, Gus Xia, Wei-Hsiang Liao, Yuki Mitsufuji, Simon Dixon
Recent advances in text-to-music editing, which employ text queries to modify music (e. g.\ by changing its style or adjusting instrumental components), present unique challenges and opportunities for AI-assisted music creation.
no code implementations • 28 May 2024 • Akio Hayakawa, Masato Ishii, Takashi Shibuya, Yuki Mitsufuji
We theoretically show that this guidance can be computed through the gradient of the optimal discriminator distinguishing real audio-video pairs from fake ones independently generated by the base models.
no code implementations • 27 May 2024 • Junyoung Seo, Kazumi Fukuda, Takashi Shibuya, Takuya Narihira, Naoki Murata, Shoukang Hu, Chieh-Hsin Lai, Seungryong Kim, Yuki Mitsufuji
In these methods, an input view is geometrically warped to novel views with estimated depth maps, then the warped image is inpainted by T2I models.
no code implementations • 23 May 2024 • Shiqi Yang, Zhi Zhong, Mengjie Zhao, Shusuke Takahashi, Masato Ishii, Takashi Shibuya, Yuki Mitsufuji
The recent audio-visual generation methods usually resort to huge large language model or composable diffusion models.
no code implementations • 23 May 2024 • Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon
In this approach, the resolution of the generator is fundamentally limited by that of the teacher DM.
Ranked #1 on Image Generation on ImageNet 32x32
no code implementations • 30 Apr 2024 • Toshimitsu Uesaka, Taiji Suzuki, Yuhta Takida, Chieh-Hsin Lai, Naoki Murata, Yuki Mitsufuji
Multimodal representation learning to integrate different modalities, such as text, vision, and audio is important for real-world applications.
no code implementations • 28 Mar 2024 • Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J. Zico Kolter
Prompt engineering is effective for controlling the output of text-to-image (T2I) generative models, but it is also laborious due to the need for manually crafted prompts.
1 code implementation • 15 Mar 2024 • Hao Hao Tan, Kin Wai Cheuk, Taemin Cho, Wei-Hsiang Liao, Yuki Mitsufuji
This paper presents enhancements to the MT3 model, a state-of-the-art (SOTA) token-based multi-instrument automatic music transcription (AMT) model.
1 code implementation • 26 Feb 2024 • Silin Gao, Mete Ismayilzada, Mengjie Zhao, Hiromi Wakaki, Yuki Mitsufuji, Antoine Bosselut
Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models.
no code implementations • 9 Feb 2024 • Yixiao Zhang, Yukara Ikemiya, Gus Xia, Naoki Murata, Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Yuki Mitsufuji, Simon Dixon
This paper introduces a novel approach to the editing of music generated by such models, enabling the modification of specific attributes, such as genre, mood and instrument, while maintaining other aspects unchanged.
no code implementations • 31 Dec 2023 • Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada, Woosung Choi, Chieh-Hsin Lai, Naoki Murata, Toshimitsu Uesaka, Kengo Uchida, Wei-Hsiang Liao, Yuki Mitsufuji
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
no code implementations • 28 Nov 2023 • Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon
Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training.
no code implementations • 20 Oct 2023 • Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, Yuki Mitsufuji
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks.
no code implementations • 2 Oct 2023 • Qiyu Wu, Mengjie Zhao, Yutong He, Lang Huang, Junya Ono, Hiromi Wakaki, Yuki Mitsufuji
In this paper, we focus on the wide existence of reporting bias in visual-language datasets, embodied as the object-attribute association, which can subsequentially degrade models trained on them.
1 code implementation • 1 Oct 2023 • Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon
Consistency Models (CM) (Song et al., 2023) accelerate score-based diffusion model sampling at the cost of sample quality but lack a natural way to trade-off quality for speed.
Ranked #3 on Image Generation on CIFAR-10
no code implementations • 27 Sep 2023 • Frank Cwitkowitz, Kin Wai Cheuk, Woosung Choi, Marco A. Martínez-Ramírez, Keisuke Toyama, Wei-Hsiang Liao, Yuki Mitsufuji
Several works have explored multi-instrument transcription as a means to bolster the performance of models on low-resource tasks, but these methods face the same data availability issues.
no code implementations • 13 Sep 2023 • Carlos Hernandez-Olivan, Koichi Saito, Naoki Murata, Chieh-Hsin Lai, Marco A. Martínez-Ramirez, Wei-Hsiang Liao, Yuki Mitsufuji
Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation.
2 code implementations • 6 Sep 2023 • Takashi Shibuya, Yuhta Takida, Yuki Mitsufuji
In the literature, it has been demonstrated that slicing adversarial network (SAN), an improved GAN training framework that can find the optimal projection, is effective in the image generation task.
Ranked #2 on Speech Synthesis on LibriTTS
no code implementations • 5 Sep 2023 • Eleonora Grassucci, Yuki Mitsufuji, Ping Zhang, Danilo Comminiello
Semantic communication is poised to play a pivotal role in shaping the landscape of future AI-driven communication systems.
2 code implementations • 14 Aug 2023 • Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai, Woosung Choi, Marco Martínez-Ramírez, WeiHsiang Liao, Igor Gadelha, Geraldo Ramos, Eddie Hsu, Hugo Rodrigues, Fabian-Robert Stöter, Alexandre Défossez, Yi Luo, Jianwei Yu, Dipam Chakraborty, Sharada Mohanty, Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva, Nabarun Goswami, Tatsuya Harada, Minseok Kim, Jun Hyung Lee, Yuanliang Dong, Xinran Zhang, Jiafeng Liu, Yuki Mitsufuji
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.
1 code implementation • 14 Aug 2023 • Stefan Uhlich, Giorgio Fabbro, Masato Hirano, Shusuke Takahashi, Gordon Wichern, Jonathan Le Roux, Dipam Chakraborty, Sharada Mohanty, Kai Li, Yi Luo, Jianwei Yu, Rongzhi Gu, Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva, Mikhail Sukhovei, Yuki Mitsufuji
A significant source of this improvement was making the simulated data better match real cinematic audio, which we further investigate in detail.
1 code implementation • 10 Jul 2023 • Keisuke Toyama, Taketo Akama, Yukara Ikemiya, Yuhta Takida, Wei-Hsiang Liao, Yuki Mitsufuji
This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content.
1 code implementation • NeurIPS 2023 • Kazuki Shimada, Archontis Politis, Parthasaarathy Sudarsanam, Daniel Krause, Kengo Uchida, Sharath Adavanne, Aapo Hakala, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Tuomas Virtanen, Yuki Mitsufuji
While direction of arrival (DOA) of sound events is generally estimated from multichannel audio data recorded in a microphone array, sound events usually derive from visually perceptible source objects, e. g., sounds of footsteps come from the feet of a walker.
no code implementations • 1 Jun 2023 • Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji, Stefano Ermon
The emergence of various notions of ``consistency'' in diffusion models has garnered considerable attention and helped achieve improved sample quality, likelihood estimation, and accelerated sampling.
no code implementations • 18 May 2023 • Hao Shi, Kazuki Shimada, Masato Hirano, Takashi Shibuya, Yuichiro Koyama, Zhi Zhong, Shusuke Takahashi, Tatsuya Kawahara, Yuki Mitsufuji
At the decoded feature level, we fuse the two decoded features by generative and predictive decoders.
1 code implementation • 13 May 2023 • Ryosuke Sawata, Naoya Takahashi, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji
We modify the target network, i. e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information.
no code implementations • 10 May 2023 • Masato Hirano, Kazuki Shimada, Yuichiro Koyama, Shusuke Takahashi, Yuki Mitsufuji
We experimentally show that our refiner can provide a clearer harmonic structure of speech and improves the reference-free metric of perceptual quality for arbitrary preceding model architectures.
1 code implementation • 3 May 2023 • Silin Gao, Beatriz Borges, Soyoung Oh, Deniz Bayazit, Saya Kanno, Hiromi Wakaki, Yuki Mitsufuji, Antoine Bosselut
They must also learn to maintain consistent speaker personas for themselves throughout the narrative, so that their counterparts feel involved in a realistic conversation or story.
no code implementations • 27 Feb 2023 • Naoya Takahashi, Mayank K. Singh, Yuki Mitsufuji
Image-to-image translation and voice conversion enable the generation of a new facial image and voice while maintaining some of the semantics such as a pose in an image and linguistic content in audio, respectively.
1 code implementation • 30 Jan 2023 • Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon
Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.
1 code implementation • 30 Jan 2023 • Yuhta Takida, Masaaki Imaizumi, Takashi Shibuya, Chieh-Hsin Lai, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives.
Ranked #1 on Image Generation on FFHQ 1024 x 1024
1 code implementation • 14 Dec 2022 • Hao-Wen Dong, Naoya Takahashi, Yuki Mitsufuji, Julian McAuley, Taylor Berg-Kirkpatrick
Further, videos in the wild often contain off-screen sounds and background noise that may hinder the model from learning the desired audio-textual correspondence.
no code implementations • 8 Nov 2022 • Koichi Saito, Naoki Murata, Toshimitsu Uesaka, Chieh-Hsin Lai, Yuhta Takida, Takao Fukui, Yuki Mitsufuji
Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations.
1 code implementation • 4 Nov 2022 • Junghyun Koo, Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Stefan Uhlich, Kyogu Lee, Yuki Mitsufuji
We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song.
1 code implementation • 27 Oct 2022 • Ryosuke Sawata, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Takashi Shibuya, Shusuke Takahashi, Yuki Mitsufuji
Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs.
1 code implementation • 23 Oct 2022 • Silin Gao, Jena D. Hwang, Saya Kanno, Hiromi Wakaki, Yuki Mitsufuji, Antoine Bosselut
Understanding rich narratives, such as dialogues and stories, often requires natural language processing systems to access relevant knowledge from commonsense knowledge graphs.
no code implementations • 20 Oct 2022 • Naoya Takahashi, Mayank Kumar Singh, Yuki Mitsufuji
We then propose a two-stage training method called Robustify that train the one-shot SVC model in the first stage on clean data to ensure high-quality conversion, and introduces enhancement modules to the encoders of the model in the second stage to enhance the feature extraction from distorted singing voices.
no code implementations • 14 Oct 2022 • Naoya Takahashi, Mayank Kumar, Singh, Yuki Mitsufuji
Recent progress in deep generative models has improved the quality of neural vocoders in speech domain.
no code implementations • 11 Oct 2022 • Kin Wai Cheuk, Ryosuke Sawata, Toshimitsu Uesaka, Naoki Murata, Naoya Takahashi, Shusuke Takahashi, Dorien Herremans, Yuki Mitsufuji
In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT).
1 code implementation • 9 Oct 2022 • Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon
Score-based generative models (SGMs) learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise.
1 code implementation • 24 Aug 2022 • Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Giorgio Fabbro, Stefan Uhlich, Chihiro Nagashima, Yuki Mitsufuji
Music mixing traditionally involves recording instruments in the form of clean, individual tracks and blending them into a final mixture using audio effects and expert knowledge (e. g., a mixing engineer).
2 code implementations • 4 Jun 2022 • Archontis Politis, Kazuki Shimada, Parthasaarathy Sudarsanam, Sharath Adavanne, Daniel Krause, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji, Tuomas Virtanen
Additionally, the report presents the baseline system that accompanies the dataset in the challenge with emphasis on the differences with the baseline of the previous iterations; namely, introduction of the multi-ACCDOA representation to handle multiple simultaneous occurences of events of the same class, and support for additional improved input features for the microphone array format.
Ranked #1 on Sound Event Localization and Detection on STARSS22
1 code implementation • 16 May 2022 • Yuhta Takida, Takashi Shibuya, WeiHsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, Yuki Mitsufuji
In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).
no code implementations • 3 Feb 2022 • Johannes Imort, Giorgio Fabbro, Marco A. Martínez Ramírez, Stefan Uhlich, Yuichiro Koyama, Yuki Mitsufuji
Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system.
2 code implementations • 14 Oct 2021 • Kazuki Shimada, Yuichiro Koyama, Shusuke Takahashi, Naoya Takahashi, Emiru Tsunoo, Yuki Mitsufuji
The multi- ACCDOA format (a class- and track-wise output format) enables the model to solve the cases with overlaps from the same class.
no code implementations • 13 Oct 2021 • Bo-Yu Chen, Wei-Han Hsu, Wei-Hsiang Liao, Marco A. Martínez Ramírez, Yuki Mitsufuji, Yi-Hsuan Yang
A central task of a Disc Jockey (DJ) is to create a mixset of mu-sic with seamless transitions between adjacent tracks.
1 code implementation • 12 Oct 2021 • Ricardo Falcon-Perez, Kazuki Shimada, Yuichiro Koyama, Shusuke Takahashi, Yuki Mitsufuji
Data augmentation methods have shown great importance in diverse supervised learning problems where labeled data is scarce or costly to obtain.
1 code implementation • 31 Aug 2021 • Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich, Fabian-Robert Stöter, Alexandre Défossez, Minseok Kim, Woosung Choi, Chin-Yun Yu, Kin-Wai Cheuk
The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i. e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers.
no code implementations • 21 Jun 2021 • Kazuki Shimada, Naoya Takahashi, Yuichiro Koyama, Shusuke Takahashi, Emiru Tsunoo, Masafumi Takahashi, Yuki Mitsufuji
This report describes our systems submitted to the DCASE2021 challenge task 3: sound event localization and detection (SELD) with directional interference.
1 code implementation • CVPR 2021 • Naoya Takahashi, Yuki Mitsufuji
In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net).
no code implementations • 26 May 2021 • Koichi Saito, Stefan Uhlich, Giorgio Fabbro, Yuki Mitsufuji
Furthermore, we propose a noise augmentation scheme for mixture-invariant training (MixIT), which allows using it also in such scenarios.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 17 Feb 2021 • Yuhta Takida, Wei-Hsiang Liao, Chieh-Hsin Lai, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative.
no code implementations • 18 Jan 2021 • Naoya Takahashi, Mayank Kumar Singh, Yuki Mitsufuji
Conventional singing voice conversion (SVC) methods often suffer from operating in high-resolution audio owing to a high dimensionality of data.
no code implementations • 1 Jan 2021 • Yuhta Takida, Wei-Hsiang Liao, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative.
1 code implementation • 21 Nov 2020 • Naoya Takahashi, Yuki Mitsufuji
In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net).
Ranked #47 on Semantic Segmentation on Cityscapes test
2 code implementations • 29 Oct 2020 • Kazuki Shimada, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji
Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target.
5 code implementations • 8 Oct 2020 • Ryosuke Sawata, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji
This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes.
Ranked #21 on Music Source Separation on MUSDB18
no code implementations • 7 Oct 2020 • Naoya Takahashi, Shota Inoue, Yuki Mitsufuji
Despite the excellent performance of neural-network-based audio source separation methods and their wide range of applications, their robustness against intentional attacks has been largely neglected.
1 code implementation • 5 Oct 2020 • Naoya Takahashi, Yuki Mitsufuji
In this paper, we claim the importance of a rapid growth of a receptive field and a simultaneous modeling of multi-resolution data in a single convolution layer, and propose a novel CNN architecture called densely connected dilated DenseNet (D3Net).
Ranked #12 on Music Source Separation on MUSDB18 (using extra training data)
1 code implementation • 29 Nov 2019 • Naoya Takahashi, Mayank Kumar Singh, Sakya Basak, Parthasaarathy Sudarsanam, Sriram Ganapathy, Yuki Mitsufuji
Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
1 code implementation • 7 Jul 2018 • Joachim Muth, Stefan Uhlich, Nathanael Perraudin, Thomas Kemp, Fabien Cardinaux, Yuki Mitsufuji
Music source separation with deep neural networks typically relies only on amplitude features.
1 code implementation • 7 May 2018 • Naoya Takahashi, Nabarun Goswami, Yuki Mitsufuji
Deep neural networks have become an indispensable technique for audio source separation (ASS).
Ranked #17 on Music Source Separation on MUSDB18 (using extra training data)
Music Source Separation Sound Audio and Speech Processing
5 code implementations • 29 Jun 2017 • Naoya Takahashi, Yuki Mitsufuji
This paper deals with the problem of audio source separation.