WiFi channel state information (CSI)-based activity recognition has sparked numerous
studies due to its widespread availability and privacy protection. However, when applied in …

Replicating analog device circuits through neural audio effect modeling has garnered
increasing interest in recent years. Existing work has predominantly focused on a one-to …

My research focuses on improving human-machine collaboration in the context of machine
learning, particularly by recognizing the limitations and potential for errors in machine …

Automatic singing voice understanding tasks, such as singer identification, singing voice
transcription, and singing technique classification, benefit from data-driven approaches that …

Significant strides have been made in creating voice identity representations using speech
data. However, the same level of progress has not been achieved for singing voices. To …

This study examines the use of breath sounds intrusively recorded by mobile devices for
person identification (PID), which is referred to as mobile-sensed BreathPID. A custom …

Cloned voices of popular singers sound increasingly realistic and have gained popularity
over the past few years. They however pose a threat to the industry due to personality rights …

Contrastive learning constitutes an emerging branch of self-supervised learning that
leverages large amounts of unlabeled data, by learning a latent space, where pairs of …

Speech remains a prevalent mode of communication powering various intelligent functions
in human-computer interaction applications, notably in Speaker/Person Identification (PID) …

Self-supervised learning has emerged as a powerful way to pre-train generalizable machine
learning models on large amounts of unlabeled data. It is particularly compelling in the …