105 citations · 115 across the 7 of their papers we have counts for
11 papers
RWCP-SSD-Onomatopoeia: Onomatopoeic Word Dataset for Environmental Sound Synthesis
Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi +3
Environmental sound synthesis is a technique for generating a natural environmental sound. Conventional work on environmental sound synthesis using sound event labels cannot finely…
Sound Event Detection Using Duration Robust Loss Function
Daichi Akiyama, Keisuke Imoto, Noriyuki Tonami +4
Many methods of sound event detection (SED) based on machine learning regard a segmented time frame as one data sample to model training. However, the sound durations of sound even…
Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto +8
In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitori…
Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation
Masahiro Yasuda, Yuma Koizumi, Shoichiro Saito +2
We propose a direction-of-arrival (DOA) estimation method for Sound Event Localization and Detection (SELD). Direct estimation of DOA using a deep neural network (DNN), i.e. comple…
Sound Event Detection by Multitask Learning of Sound Events and Scenes with Soft Scene Labels
Keisuke Imoto, Noriyuki Tonami, Yuma Koizumi +3
Sound event detection (SED) and acoustic scene classification (ASC) are major tasks in environmental sound analysis. Considering that sound events and scenes are closely related to…
Graph Cepstrum: Spatial Feature Extracted from Partially Connected Microphones
Keisuke Imoto
In this paper, we propose an effective and robust method of spatial feature extraction for acoustic scene analysis utilizing partially synchronized and/or closely located distribut…