143 citations · 527 across the 38 of their papers we have counts for
5 papers · 1 filter
SNRi Target Training for Joint Speech Enhancement and Recognition
Yuma Koizumi, Shigeki Karita, Arun Narayanan +2
Speech enhancement (SE) is used as a frontend in speech applications including automatic speech recognition (ASR) and telecommunication. A difficulty in using the SE frontend is th…
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
Yuma Koizumi, Shigeki Karita, Scott Wisdom +4
Single-channel speech enhancement (SE) is an important task in speech processing. A widely used framework combines an analysis/synthesis filterbank with a mask prediction network,…
Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions
Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi +6
We present the task description and discussion on the results of the DCASE 2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sound detection (ASD) task, identi…
Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method
Koichi Saito, Tomohiko Nakamura, Kohei Yatabe +2
Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the v…
Noisy-target Training: A Training Strategy for DNN-based Speech Enhancement without Clean Speech
Takuya Fujimura, Yuma Koizumi, Kohei Yatabe +1
Deep neural network (DNN)-based speech enhancement ordinarily requires clean speech signals as the training target. However, collecting clean signals is very costly because they mu…