159 citations · 402 across the 20 of their papers we have counts for
23 papers
In-Ear Electrode EEG for Practical SSVEP BCI
Surej Mouli, Ramaswamy Palaniappan, Emmanuel Molefi +1
Steady State Visual Evoked Potential (SSVEP) methods for brain computer interfaces (BCI) are popular due to higher information transfer rate and easier setup with minimal training,…
Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG
Ramaswamy Palaniappan, Surej Mouli, Howard Bowman +1
Half of all road accidents result from either lack of driver attention or from maintaining insufficient separation between vehicles. Collision from the rear, in particular, has bee…
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
Zhifu Gao, Shiliang Zhang, Ian McLoughlin +1
Transformers have recently dominated the ASR field. Although able to yield good performance, they involve an autoregressive (AR) decoder to generate tokens one by one, which is com…
An Ensemble of Deep Learning Frameworks Applied For Predicting Respiratory Anomalies
Lam Pham, Dat Ngo, Truong Hoang +2
In this paper, we evaluate various deep learning frameworks for detecting respiratory anomalies from input audio recordings. To this end, we firstly transform audio respiratory cyc…
Extremely Low Footprint End-to-End ASR System for Smart Device
Zhifu Gao, Yiwu Yao, Shiliang Zhang +3
Recently, end-to-end (E2E) speech recognition has become popular, since it can integrate the acoustic, pronunciation and language models into a single neural network, which outperf…
Multi-view Audio and Music Classification
Huy Phan, Huy Le Nguyen, Oliver Y. Chén +4
We propose in this work a multi-view learning approach for audio and music classification. Considering four typical low-level representations (i.e. different views) commonly used f…
Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model
Zhifu Gao, Shiliang Zhang, Ming Lei +1
Recently, online end-to-end ASR has gained increasing attention. However, the performance of online systems still lags far behind that of offline systems, with a large gap in quali…
Incandescent Bulb and LED Brake Lights:Novel Analysis of Reaction Times
Ramaswamy Palaniappan, Surej Mouli, Evangelina Fringi +2
Rear-end collision accounts for around 8% of all vehicle crashes in the UK, with the failure to notice or react to a brake light signal being a major contributory cause. Meanwhile…
Self-Attention Generative Adversarial Network for Speech Enhancement
Huy Phan, Huy Le Nguyen, Oliver Y. Chén +4
Existing generative adversarial networks (GANs) for speech enhancement solely rely on the convolution operation, which may obscure temporal dependencies across the sequence input.…
On Multitask Loss Function for Audio Event Detection and Localization
Huy Phan, Lam Pham, Philipp Koch +3
Audio event localization and detection (SELD) have been commonly tackled using multitask models. Such a model usually consists of a multi-label event classification branch with sig…
SAN-M: Memory Equipped Self-Attention for End-to-End Speech Recognition
Zhifu Gao, Shiliang Zhang, Ming Lei +1
End-to-end speech recognition has become popular in recent years, since it can integrate the acoustic, pronunciation and language models into a single neural network. Among end-to-…
Deep Feature Embedding and Hierarchical Classification for Audio Scene Classification
Lam Pham, Ian McLoughlin, Huy Phan +2
In this work, we propose an approach that features deep feature embedding learning and hierarchical classification with triplet loss function for Acoustic Scene Classification (ASC…
Robust Acoustic Scene Classification using a Multi-Spectrogram Encoder-Decoder Framework
Lam Pham, Huy Phan, Truc Nguyen +3
This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature.…
Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases
Lam Pham, Ian McLoughlin, Huy Phan +3
This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves…
Improving GANs for Speech Enhancement
Huy Phan, Ian V. McLoughlin, Lam Pham +4
Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use…
LSTM-TDNN with convolutional front-end for Dialect Identification in the 2019 Multi-Genre Broadcast Challenge
Xiaoxiao Miao, Ian McLoughlin
This paper presents a novel Dialect Identification (DID) system developed for the Fifth Edition of the Multi-Genre Broadcast challenge, the task of Fine-grained Arabic Dialect Iden…
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
Huy Phan, Oliver Y. Chén, Philipp Koch +4
Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a…
Spatio-Temporal Attention Pooling for Audio Scene Classification
Huy Phan, Oliver Y. Chén, Lam Pham +4
Acoustic scenes are rich and redundant in their content. In this work, we present a spatio-temporal attention pooling layer coupled with a convolutional recurrent neural network to…
Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
Huy Phan, Oliver Y. Chén, Philipp Koch +4
Due to the variability in characteristics of audio scenes, some scenes can naturally be recognized earlier than others. In this work, rather than using equal-length snippets for al…
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
Huy Phan, Oliver Y. Chén, Philipp Koch +4
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages…
On the Use of a Spectral Glottal Model for the Source-filter Separation of Speech
Olivier Perrotin, Ian Vince McLoughlin
The estimation of glottal flow from a speech waveform is a key method for speech analysis and parameterization. Significant research effort has been made to dissociate the first vo…
Enabling Early Audio Event Detection with Neural Networks
Huy Phan, Philipp Koch, Ian McLoughlin +1
This paper presents a methodology for early detection of audio events from audio streams. Early detection is the ability to infer an ongoing event during its initial stage. The pro…
Learning Compact Structural Representations for Audio Events Using Regressor Banks
Huy Phan, Marco Maass, Lars Hertel +3
We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on…