In the context of passive acoustic monitoring (PAM) better models are needed to reliably
gain insights from large amounts of raw, unlabeled data. Bioacoustics foundation models …

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

State-of-the-art anomalous sound detection systems often utilize angular margin losses to
learn suitable representations of acoustic data using an auxiliary task, which usually is a …

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 …

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 …

Due to the high variation in the application requirements of sound event detection (SED)
systems, it is not sufficient to evaluate systems only in a single operating mode. Therefore …

Sound event detection is the task of recognizing sounds and determining their extent
(onset/offset times) within an audio clip. Existing systems commonly predict sound presence …

Text-to-audio grounding (TAG) task aims to predict the onsets and offsets of sound events
described by natural language. This task can facilitate applications such as multimodal …

The sense of hearing is fundamental to human beings, as it allows them to perceive their
surroundings. However, this simple task of recognizing different sounds in complex …