6 citations · 12 across the 17 of their papers we have counts for
18 papers
COPE: Continual Personalization of LLMs under Sparse User Feedback via User Embeddings and Self-Evaluation
Ruike Cao, Fugen Yao, Liang Dong +3
While Large Language Models (LLMs) have achieved remarkable results across various benchmarks, their alignment with normative values often results in homogenized responses that fai…
Misrecognition or Abstraction? Rethinking Outputs of Sound Event Recognition
Naoya Tomida, Yuki Okamoto, Keisuke Imoto
Conventional general sound recognition systems typically output deterministic sound event labels, implicitly assuming that the target sound class can be correctly identified from t…
Sound Scene Synthesis at the DCASE 2024 Challenge
Mathieu Lagrange, Junwon Lee, Modan Tailleur +5
This paper presents Task 7 at the DCASE 2024 Challenge: sound scene synthesis. Recent advances in sound synthesis and generative models have enabled the creation of realistic and d…
Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation
Junwon Lee, Modan Tailleur, Laurie M. Heller +5
Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene…
Construction and Analysis of Impression Caption Dataset for Environmental Sounds
Yuki Okamoto, Ryotaro Nagase, Minami Okamoto +4
Some datasets with the described content and order of occurrence of sounds have been released for conversion between environmental sound and text. However, there are very few texts…
Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant
Modan Tailleur, Junwon Lee, Mathieu Lagrange +4
This paper explores whether considering alternative domain-specific embeddings to calculate the Fréchet Audio Distance (FAD) metric can help the FAD to correlate better with percep…
CAPTDURE: Captioned Sound Dataset of Single Sources
Yuki Okamoto, Kanta Shimonishi, Keisuke Imoto +3
In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model traini…
Environmental sound synthesis from vocal imitations and sound event labels
Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi +3
One way of expressing an environmental sound is using vocal imitations, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice. We can effect…
Foley Sound Synthesis at the DCASE 2023 Challenge
Keunwoo Choi, Jaekwon Im, Laurie Heller +5
The addition of Foley sound effects during post-production is a common technique used to enhance the perceived acoustic properties of multimedia content. Traditionally, Foley sound…
Visual onoma-to-wave: environmental sound synthesis from visual onomatopoeias and sound-source images
Hien Ohnaka, Shinnosuke Takamichi, Keisuke Imoto +3
We propose a method for synthesizing environmental sounds from visually represented onomatopoeias and sound sources. An onomatopoeia is a word that imitates a sound structure, i.e.…
How Should We Evaluate Synthesized Environmental Sounds
Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi +2
Although several methods of environmental sound synthesis have been proposed, there has been no discussion on how synthesized environmental sounds should be evaluated. Only either…
Environmental Sound Extraction Using Onomatopoeic Words
Yuki Okamoto, Shota Horiguchi, Masaaki Yamamoto +2
An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We…
Sound Event Detection Guided by Semantic Contexts of Scenes
Noriyuki Tonami, Keisuke Imoto, Ryotaro Nagase +3
Some studies have revealed that contexts of scenes (e.g., "home," "office," and "cooking") are advantageous for sound event detection (SED). Mobile devices and sensing technologies…
Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events
Noriyuki Tonami, Keisuke Imoto, Yuki Okamoto +2
In conventional sound event detection (SED) models, two types of events, namely, those that are present and those that do not occur in an acoustic scene, are regarded as the same t…
Onoma-to-wave: Environmental sound synthesis from onomatopoeic words
Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi +3
In this paper, we propose a framework for environmental sound synthesis from onomatopoeic words. As one way of expressing an environmental sound, we can use an onomatopoeic word, w…
RWCP-SSD-Onomatopoeia: Onomatopoeic Word Dataset for Environmental Sound Synthesis
Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi +3
Environmental sound synthesis is a technique for generating a natural environmental sound. Conventional work on environmental sound synthesis using sound event labels cannot finely…
Sound Event Detection Using Duration Robust Loss Function
Daichi Akiyama, Keisuke Imoto, Noriyuki Tonami +4
Many methods of sound event detection (SED) based on machine learning regard a segmented time frame as one data sample to model training. However, the sound durations of sound even…
Overview of Tasks and Investigation of Subjective Evaluation Methods in Environmental Sound Synthesis and Conversion
Yuki Okamoto, Keisuke Imoto, Tatsuya Komatsu +4
Synthesizing and converting environmental sounds have the potential for many applications such as supporting movie and game production, data augmentation for sound event detection…