Sound event detection (SED) is an interesting but challenging task due to the scarcity of data
and diverse sound events in real life. In this paper, we focus on the semi-supervised SED …

This report proposes a frequency dynamic convolution (FDY) with a large kernel attention
(LKA)-convolutional recurrent neural network (CRNN) with a pre-trained bidirectional …

In this paper, we propose an effective sound event detection (SED) method based on the
audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for …

WiFi-based human pose estimation (HPE) has emerged as a promising alternative to
conventional vision-based techniques, yet faces the high computational cost hindering its …

This report presents the systems developed and submitted by Fortemedia Singapore
(FMSG) for DCASE 2023 Task 4A, which focuses on sound event detection with weak labels …

This report presents the systems developed and submitted by Fortemedia Singapore
(FMSG) and Joint Laboratory of Environmental Sound Sensing (JLESS) for DCASE 2024 …

Recently, convolutional neural networks (CNNs) have been widely used in sound event
detection (SED). However, traditional convolution is deficient in learning time-frequency …

Jointly learning from a small labeled set and a larger unlabeled set is an active research
topic under semi-supervised learning (SSL). In this paper, we propose a novel SSL method …

In this technical report, we present our submissions for DCASE 2023 challenge task4a. We
mainly study how to fine-tune patchout fast spectrogram transformer (PaSST) for sound …

Sound event detection (SED) often suffers from the data deficiency problem. Recent SED
systems leverage the large pretrained self-supervised learning (SelfSL) models to mitigate …