117 citations · 245 across the 23 of their papers we have counts for
25 papers
XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
Ioan-Paul Ciobanu, Andrei-Iulian Hiji, Nicolae-Catalin Ristea +3
Recent advances in audio generation led to an increasing number of deepfakes, making the general public more vulnerable to financial scams, identity theft, and misinformation. Audi…
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru +7
We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content. We identify various kinds of deepfakes and co…
ICASSP 2024 Speech Signal Improvement Challenge
Nicolae Catalin Ristea, Ando Saabas, Ross Cutler +3
The ICASSP 2024 Speech Signal Improvement Grand Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. This marks…
Cascaded Cross-Modal Transformer for Audio-Textual Classification
Nicolae-Catalin Ristea, Andrei Anghel, Radu Tudor Ionescu
Speech classification tasks often require powerful language understanding models to grasp useful features, which becomes problematic when limited training data is available. To att…
ICASSP 2023 Acoustic Echo Cancellation Challenge
Ross Cutler, Ando Saabas, Tanel Parnamaa +7
The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is…
Multi-dimensional Speech Quality Assessment in Crowdsourcing
Babak Naderi, Ross Cutler, Nicolae-Catalin Ristea
Subjective speech quality assessment is the gold standard for evaluating speech enhancement processing and telecommunication systems. The commonly used standard ITU-T Rec. P.800 de…
RoDia: A New Dataset for Romanian Dialect Identification from Speech
Codrut Rotaru, Nicolae-Catalin Ristea, Radu Tudor Ionescu
We introduce RoDia, the first dataset for Romanian dialect identification from speech. The RoDia dataset includes a varied compilation of speech samples from five distinct regions…
CL-MAE: Curriculum-Learned Masked Autoencoders
Neelu Madan, Nicolae-Catalin Ristea, Kamal Nasrollahi +2
Masked image modeling has been demonstrated as a powerful pretext task for generating robust representations that can be effectively generalized across multiple downstream tasks. T…
Cascaded Cross-Modal Transformer for Request and Complaint Detection
Nicolae-Catalin Ristea, Radu Tudor Ionescu
We propose a novel cascaded cross-modal transformer (CCMT) that combines speech and text transcripts to detect customer requests and complaints in phone conversations. Our approach…
Sea Ice Segmentation From SAR Data by Convolutional Transformer Networks
Nicolae-Catalin Ristea, Andrei Anghel, Mihai Datcu
Sea ice is a crucial component of the Earth's climate system and is highly sensitive to changes in temperature and atmospheric conditions. Accurate and timely measurement of sea ic…
DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation
Evgenii Indenbom, Nicolae-Catalin Ristea, Ando Saabas +3
Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation (DR) are an integral part of modern full-duplex communication systems. As the demand for teleconferenci…
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
Nicolae-Catalin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu +3
We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level. The novelty of the proposed model is threef…
Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation
Florinel-Alin Croitoru, Nicolae-Catalin Ristea, Dana Dascalescu +3
We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher…
Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval
Nicolae-Cătălin Ristea, Andrei Anghel, Mihai Datcu +1
Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many g…
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
Neelu Madan, Nicolae-Catalin Ristea, Radu Tudor Ionescu +4
Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on indu…
Learning Rate Curriculum
Florinel-Alin Croitoru, Nicolae-Catalin Ristea, Radu Tudor Ionescu +1
Most curriculum learning methods require an approach to sort the data samples by difficulty, which is often cumbersome to perform. In this work, we propose a novel curriculum learn…
Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery
Nicolae-Catalin Ristea, Andrei Anghel, Mihai Datcu +1
Spaceborne synthetic aperture radar can provide meters scale images of the ocean surface roughness day or night in nearly all weather conditions. This makes it a unique asset for m…
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution
Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Andreea-Iuliana Miron +4
Super-resolving medical images can help physicians in providing more accurate diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging (MRI) techniqu…
SepTr: Separable Transformer for Audio Spectrogram Processing
Nicolae-Catalin Ristea, Radu Tudor Ionescu, Fahad Shahbaz Khan
Following the successful application of vision transformers in multiple computer vision tasks, these models have drawn the attention of the signal processing community. This is bec…
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Nicolae-Catalin Ristea, Neelu Madan, Radu Tudor Ionescu +4
Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abno…
CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation
Nicolae-Catalin Ristea, Andreea-Iuliana Miron, Olivian Savencu +4
We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans and the other way around. Solving this task has two important app…
Self-paced ensemble learning for speech and audio classification
Nicolae-Catalin Ristea, Radu Tudor Ionescu
Combining multiple machine learning models into an ensemble is known to provide superior performance levels compared to the individual components forming the ensemble. This is beca…
Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional Networks
Nicolae-Cătălin Ristea, Andrei Anghel, Radu Tudor Ionescu
Radar sensors are gradually becoming a wide-spread equipment for road vehicles, playing a crucial role in autonomous driving and road safety. The broad adoption of radar sensors in…
Non-linear Neurons with Human-like Apical Dendrite Activations
Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Nicolae-Catalin Ristea +1
In order to classify linearly non-separable data, neurons are typically organized into multi-layer neural networks that are equipped with at least one hidden layer. Inspired by som…