265 citations · 532 across the 34 of their papers we have counts for
26 papers · 1 filter
Using UMAP to Inspect Audio Data for Unsupervised Anomaly Detection under Domain-Shift Conditions
Andres Fernandez, Mark D. Plumbley
The goal of Unsupervised Anomaly Detection (UAD) is to detect anomalous signals under the condition that only non-anomalous (normal) data is available beforehand. In UAD under Doma…
Federated Learning With Highly Imbalanced Audio Data
Marc C. Green, Mark D. Plumbley
Federated learning (FL) is a privacy-preserving machine learning method that has been proposed to allow training of models using data from many different clients, without these cli…
CAA-Net: Conditional Atrous CNNs with Attention for Explainable Device-robust Acoustic Scene Classification
Zhao Ren, Qiuqiang Kong, Jing Han +2
Acoustic Scene Classification (ASC) aims to classify the environment in which the audio signals are recorded. Recently, Convolutional Neural Networks (CNNs) have been successfully…
Gender Bias in Depression Detection Using Audio Features
Andrew Bailey, Mark D. Plumbley
Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression. Datasets such as Distress Analysis Interview C…
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection
Yin Cao, Turab Iqbal, Qiuqiang Kong +3
Polyphonic sound event localization and detection (SELD), which jointly performs sound event detection (SED) and direction-of-arrival (DoA) estimation, detects the type and occurre…
Learning with Out-of-Distribution Data for Audio Classification
Turab Iqbal, Yin Cao, Qiuqiang Kong +2
In supervised machine learning, the assumption that training data is labelled correctly is not always satisfied. In this paper, we investigate an instance of labelling error for cl…