Search Results for author: Yuichiro Koyama

Found 10 papers, 5 papers with code

STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events

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.

Sound Event Localization and Detection

Diffusion-based Signal Refiner for Speech Separation

no code implementations10 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.

Denoising Speech Enhancement +1

STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events

2 code implementations4 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.

Sound Event Localization and Detection

Distortion Audio Effects: Learning How to Recover the Clean Signal

no code implementations3 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.

Music Source Separation

Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Events

no code implementations30 Oct 2019 Kazuki Shimada, Yuichiro Koyama, Akira Inoue

Few-shot learning systems for sound event recognition have gained interests since they require only a few examples to adapt to new target classes without fine-tuning.

Few-Shot Learning Metric Learning

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