143 citations · 511 across the 26 of their papers we have counts for
6 papers · 1 filter
Invertible DNN-based nonlinear time-frequency transform for speech enhancement
Daiki Takeuchi, Kohei Yatabe, Yuma Koizumi +2
We propose an end-to-end speech enhancement method with trainable time-frequency~(T-F) transform based on invertible deep neural network~(DNN). The resent development of speech enh…
First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation
Luca Mazzon, Yuma Koizumi, Masahiro Yasuda +1
In this paper, we propose a novel data augmentation method for training neural networks for Direction of Arrival (DOA) estimation. This method focuses on expanding the representati…
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu +2
This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS). To the best our knowledge, no large-scale datasets are ava…
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds
Yuma Koizumi, Shoichiro Saito, Masataka Yamaguchi +2
Use of an autoencoder (AE) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds (ADS). The AE is trained to minimize the sample mean of th…
Data-driven design of perfect reconstruction filterbank for DNN-based sound source enhancement
Daiki Takeuchi, Kohei Yatabe, Yuma Koizumi +2
We propose a data-driven design method of perfect-reconstruction filterbank (PRFB) for sound-source enhancement (SSE) based on deep neural network (DNN). DNNs have been used to est…
Deep Griffin-Lim Iteration
Yoshiki Masuyama, Kohei Yatabe, Yuma Koizumi +2
This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN). T…