14 citations · 19 across the 7 of their papers we have counts for
7 papers
AI-assisted summary of suicide risk Formulation
Rajib Rana, Niall Higgins, Kazi N. Haque +5
Background: Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and development of an individual's…
Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)
Kazi Nazmul Haque, Rajib Rana, Tasnim Jarin +1
This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised fea…
Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning
Rajib Rana, Niall Higgins, Kazi Nazmul Haque +4
Ensuring accurate call prioritisation is essential for optimising the efficiency and responsiveness of mental health helplines. Currently, call operators rely entirely on the calle…
High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder
Kazi Nazmul Haque, Rajib Rana, Björn W Schuller
Unsupervised disentangled representation learning from the unlabelled audio data, and high fidelity audio generation have become two linchpins in the machine learning research fiel…
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data
Kazi Nazmul Haque, Rajib Rana, John H. L. Hansen +1
Recent improvements in Generative Adversarial Neural Networks (GANs) have shown their ability to generate higher quality samples as well as to learn good representations for transf…
Disentangled Representation Learning with Information Maximizing Autoencoder
Kazi Nazmul Haque, Siddique Latif, Rajib Rana
Learning disentangled representation from any unlabelled data is a non-trivial problem. In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learn…