11 accepted papers
Sound Event Localization and Detection (SELD) is the combined task of detecting sound events and estimating their spatial locations. We propose a Sound source Distance Estimation (SDE) method for SELD that utilizes a physics-informed prior. The conventional data-driven approach of SDE for SELD can h…
Sound Event Localization and Detection (SELD) is the task of estimating the class, duration, and direction of arrival (DOA) of sound events. State-of-the-art SELD systems use a data-driven approach based on Deep Neural Networks (DNNs) to deal with complex situations involving overlapping and moving…
We aim to perform sound event localization and detection (SELD) using wearable equipment for a moving human, such as a pedestrian. Conventional SELD tasks have dealt only with microphone arrays located in static positions. However, self-motion with three rotational and three translational degrees of…
Target Sound Extraction (TSE) is a technique for extracting sound events belonging to a target sound class in a mixture using a Deep Neural Network (DNN). Offline TSE that uses non-causal models has achieved high extraction performance. However, many applications require online processing. Simply co…
In sound event detection (SED), the representation ability of deep neural network (DNN) models must be increased to significantly improve the accuracy or increase the number of classifiable classes. When building large-scale DNN models, a highly parameter-efficient DNN architecture should preferably…
Our goal is to develop a sound event localization and detection (SELD) system that works robustly in unknown environments. A SELD system trained on known environment data is degraded in an unknown environment due to environmental effects such as reverberation and noise not contained in the training…
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. In this task, distributed sensors are utilized complementarily to capture events that are di…
Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with the sound signals observed by a microphone array. Although ambisonic microphones are popular in the literature of SELD, t…
We propose a similarity function for one-shot anomaly detection in sounds (ADS) called SPecific anomaly IDentifiER network (SPIDERnet). In ADS systems, since overlooking an anomaly may result in serious incidents, we need to update such systems using an (often only one) overlooked anomalous sample.…
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. completely-datadriven approach, achieves high accuracy. However, there is a gap in the accuracy between DOA estimation for sin…
In anomaly detection systems, overlooking anomalies may result in serious incidents. Thus, when a system overlooks an anomaly, we need to update the system to never overlook the observed type of anomalies twice. There are roughly two possible approaches to solve this problem; re-training the whole s…