Search Results for author:
Found 11 papers, 6 papers with code
Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
This is because the information obtained from a single sensor is often missing or fragmented in such an environment; observations from multiple locations and modalities should be integrated to analyze events comprehensively.
6DoF SELD: Sound Event Localization and Detection Using Microphones and Motion Tracking Sensors on self-motioning human
A system trained only with a dataset using microphone arrays in a fixed position would be unable to adapt to the fast relative motion of sound events associated with self-motion, resulting in the degradation of SELD performance.
Multi-view and Multi-modal Event Detection Utilizing Transformer-based Multi-sensor fusion
We tackle a challenging task: multi-view and multi-modal event detection that detects events in a wide-range real environment by utilizing data from distributed cameras and microphones and their weak labels.
Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments
Our goal is to develop a sound event localization and detection (SELD) system that works robustly in unknown environments.
Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head
Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction.
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions
This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS).
A Transformer-based Audio Captioning Model with Keyword Estimation
TRACKE estimates keywords, which comprise a word set corresponding to audio events/scenes in the input audio, and generates the caption while referring to the estimated keywords to reduce word-selection indeterminacy.
DOA Estimation by DNN-based Denoising and Dereverberation from Sound Intensity Vector
We propose a direction of arrival (DOA) estimation method that combines sound-intensity vector (IV)-based DOA estimation and DNN-based denoising and dereverberation.
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
To build a large-scale dataset for ADMOS, we collected anomalous operating sounds of miniature machines (toys) by deliberately damaging them.
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds
The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch.
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm.
