Search Results for author:
Found 23 papers, 2 papers with code
Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community
In this paper, an approach for hate speech detection against women in the Arabic community on social media (e. g. Youtube) is proposed.
BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis
This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields.
Cooperation Is All You Need
Going beyond 'dendritic democracy', we introduce a 'democracy of local processors', termed Cooperator.
Unlocking the potential of two-point cells for energy-efficient and resilient training of deep nets
Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as 1999.
A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids
In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology.
PL-kNN: A Parameterless Nearest Neighbors Classifier
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes.
Multimodal Speech Enhancement Using Burst Propagation
This paper proposes the MBURST, a novel multimodal solution for audio-visual speech enhancements that consider the most recent neurological discoveries regarding pyramidal cells of the prefrontal cortex and other brain regions.
Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing
Deep learning (DL) has big-data processing capabilities that are as good, or even better, than those of humans in many real-world domains, but at the cost of high energy requirements that may be unsustainable in some applications and of errors, that, though infrequent, can be large.
Canonical Cortical Graph Neural Networks and its Application for Speech Enhancement in Audio-Visual Hearing Aids
Despite the recent success of machine learning algorithms, most models face drawbacks when considering more complex tasks requiring interaction between different sources, such as multimodal input data and logical time sequences.
A Novel Speech Intelligibility Enhancement Model based on CanonicalCorrelation and Deep Learning
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals.
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
Our proposed AV CCA-GNN model deals with multimodal representation learning context.
A Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning for Hearing-Assistive Technologies
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are generally trained to minimise the distance between clean and enhanced speech features.
Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English
In this paper, an approach for hate speech detection against women in Arabic community on social media (e. g. Youtube) is proposed.
AV Speech Enhancement Challenge using a Real Noisy Corpus
This paper presents, a first of its kind, audio-visual (AV) speech enhacement challenge in real-noisy settings.
CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement
In addition, our work challenges a popular belief that a scarcity of multi-language large vocabulary AV corpus and wide variety of noises is a major bottleneck to build a robust language, speaker and noise independent SE systems.
Role of Awareness and Universal Context in a Spiking Conscious Neural Network (SCNN): A New Perspective and Future Directions
It is believed that the conscious neuron inherently contains enough knowledge about the situation in which the problem is to be solved based on past learning and reasoning and it defines the precise role of incoming multisensory signals to originate a precise neural firing (exhibiting switch-like behaviour).
Arabizi sentiment analysis based on transliteration and automatic corpus annotation
Afterwards, we automatically classify the sentiment of the transliterated corpus using an automatically annotated corpus.
Contextual Audio-Visual Switching For Speech Enhancement in Real-World Environments
In this paper, we introduce a novel contextual AV switching component that contextually exploits AV cues with respect to different operating conditions to estimate clean audio, without requiring any SNR estimation.
Saliency Detection via Bidirectional Absorbing Markov Chain
Simultaneously, foreground prior as the virtual absorbing nodes is used to calculate the absorption time and obtain the background possibility.
SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis
In this paper, we present a novel approach to automatically construct an annotated sentiment corpus for Algerian dialect (a Maghrebi Arabic dialect).
Exploiting Deep Learning for Persian Sentiment Analysis
The rise of social media is enabling people to freely express their opinions about products and services.
Lip-Reading Driven Deep Learning Approach for Speech Enhancement
The proposed audio-visual (AV) speech enhancement framework operates at two levels.
DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation
The process of selective attention in the brain is known to contextually exploit the available audio and visual cues to better focus on target speaker while filtering out other noises.
