8 papers · 2 filters
Online Algorithms for Independent Low-Rank Matrix Analysis and Rank-Constrained Spatial Covariance Matrix Estimation Based on Maximum Weighted Likelihood Estimation
Yuto Ishikawa, Norihiro Takamune, Tomohiko Nakamura +4
Real-time multichannel speech extraction (MSE) under diffuse noise conditions is an important task with a wide range of applications, such as speech recognition and hearing aids. I…
Differentiable Digital Signal Processing Mixture Model-Guided Diffusion for Synthesis Parameter Estimation from Harmonic Sound Mixtures
Kengo Takemoto, Tomohiko Nakamura, Hiroshi Saruwatari
A differentiable digital signal processing (DDSP) autoencoder reconstructs a monophonic harmonic sound through three types of synthesis parameters: fundamental frequency, loudness,…
Lead Vocal Separation from Vocal Ensemble Mixtures Using Phoneme Alignment
Yuma Narahata, Tomohiko Nakamura, Yuki Saito +1
Contemporary a cappella singing often has a lead-and-accompaniment texture, where the lead vocal (Vo) part carries the main melody and the remaining vocal parts provide accompanime…
Auditing Protocol-Level Shortcuts in Large Audio Language Model Judges for Speech Evaluation
Joonyong Park, David M. Chan, Yuki Saito +1
Large audio-language models (LALMs) are increasingly used as automatic judges for speech evaluation. However, high agreement with human ratings does not guarantee that their verdic…
DialogueSidon: Recovering Full-Duplex Dialogue Tracks from In-the-Wild Dialogue Audio
Wataru Nakata, Yuki Saito, Kazuki Yamauchi +2
Full-duplex dialogue audio, in which each speaker is recorded on a separate track, is an important resource for spoken dialogue research, but is difficult to collect at scale. Most…
Geneses: Unified Generative Speech Enhancement and Separation
Kohei Asai, Wataru Nakata, Yuki Saito +1
Real-world audio recordings often contain multiple speakers and various degradations, which limit both the quantity and quality of speech data available for building state-of-the-a…
Dissecting Performance Degradation in Audio Source Separation under Sampling Frequency Mismatch
Kanami Imamura, Tomohiko Nakamura, Kohei Yatabe +1
Audio processing methods based on deep neural networks are typically trained at a single sampling frequency (SF). To handle untrained SFs, signal resampling is commonly employed, b…
DistilMOS: Layer-Wise Self-Distillation For Self-Supervised Learning Model-Based MOS Prediction
Jianing Yang, Wataru Nakata, Yuki Saito +1
With the advancement of self-supervised learning (SSL), fine-tuning pretrained SSL models for mean opinion score (MOS) prediction has achieved state-of-the-art performance. However…