4 citations · 4 across the 9 of their papers we have counts for
9 papers
Do Music Foundation Models Embed Pitch in Helical Structure?
Hayato Yagi, Shinnosuke Takamichi, Rin Sato +2
This study analyzes the intermediate representations of music foundation models (MFMs) and reports the geometric structures used to represent pitch information. By inputting isolat…
Cross-lingual Data Selection Using Clip-level Acoustic Similarity for Enhancing Low-resource Automatic Speech Recognition
Shunsuke Mitsumori, Sara Kashiwagi, Keitaro Tanaka +1
This paper presents a novel donor data selection method to enhance low-resource automatic speech recognition (ASR). While ASR performs well in high-resource languages, its accuracy…
Formula-Supervised Sound Event Detection: Pre-Training Without Real Data
Yuto Shibata, Keitaro Tanaka, Yoshiaki Bando +3
In this paper, we propose a novel formula-driven supervised learning (FDSL) framework for pre-training an environmental sound analysis model by leveraging acoustic signals parametr…
SyncViolinist: Music-Oriented Violin Motion Generation Based on Bowing and Fingering
Hiroki Nishizawa, Keitaro Tanaka, Asuka Hirata +4
Automatically generating realistic musical performance motion can greatly enhance digital media production, often involving collaboration between professionals and musicians. Howev…
Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability
Taichi Higasa, Keitaro Tanaka, Qi Feng +1
Language learners should regularly engage in reading challenging materials as part of their study routine. Nevertheless, constantly referring to dictionaries is time-consuming and…
Audio-Visual Speech Enhancement With Selective Off-Screen Speech Extraction
Tomoya Yoshinaga, Keitaro Tanaka, Shigeo Morishima
This paper describes an audio-visual speech enhancement (AV-SE) method that estimates from noisy input audio a mixture of the speech of the speaker appearing in an input video (on-…
Improving the Gap in Visual Speech Recognition Between Normal and Silent Speech Based on Metric Learning
Sara Kashiwagi, Keitaro Tanaka, Qi Feng +1
This paper presents a novel metric learning approach to address the performance gap between normal and silent speech in visual speech recognition (VSR). The difference in lip movem…
Memory Efficient Diffusion Probabilistic Models via Patch-based Generation
Shinei Arakawa, Hideki Tsunashima, Daichi Horita +2
Diffusion probabilistic models have been successful in generating high-quality and diverse images. However, traditional models, whose input and output are high-resolution images, s…
Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex
Keitaro Tanaka, Ryosuke Sawata, Shusuke Takahashi
This paper presents a new deep clustering (DC) method called manifold-aware DC (M-DC) that can enhance hyperspace utilization more effectively than the original DC. The original DC…