We proposed Audio Difference Captioning (ADC) as a new extension task of...
Self-supervised learning general-purpose audio representations have
demo...
We present the task description of the Detection and Classification of
A...
This paper proposes a deep sound-field denoiser, a deep neural network (...
This paper provides a baseline system for First-shot-compliant unsupervi...
Masked Autoencoders is a simple yet powerful self-supervised learning me...
We propose a novel framework for target speech extraction based on seman...
The amount of audio data available on public websites is growing rapidly...
We present the task description of the Detection and Classification of
A...
Many application studies rely on audio DNN models pre-trained on a
large...
Recent general-purpose audio representations show state-of-the-art
perfo...
Pre-trained models are essential as feature extractors in modern machine...
We tackle a challenging task: multi-view and multi-modal event detection...
We present the task description and discussion on the results of the DCA...
Inspired by the recent progress in self-supervised learning for computer...
The system we used for Task 6 (Automated Audio Captioning)of the Detecti...
This technical report describes the system participating to the Detectio...
This paper presents the details of the DCASE 2020 Challenge Task 2;
Unsu...
We propose a speech enhancement method using a causal deep neural
networ...
Phase reconstruction, which estimates phase from a given amplitude
spect...
We propose an end-to-end speech enhancement method with trainable
time-f...
In this paper, we propose a novel data augmentation method for training
...
This paper introduces a new dataset called "ToyADMOS" designed for anoma...
Use of an autoencoder (AE) as a normal model is a state-of-the-art techn...
We propose a data-driven design method of perfect-reconstruction filterb...
This paper presents a novel phase reconstruction method (only from a giv...
We tackle unsupervised anomaly detection (UAD), a problem of detecting d...
This study proposes a trainable adaptive window switching (AWS) method a...
This paper proposes a novel optimization principle and its implementatio...