1 code implementation • 17 Jan 2024 • Stefano Damiano, Luca Bondi, Shabnam Ghaffarzadegan, Andre Guntoro, Toon van Waterschoot
In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency.
no code implementations • 19 May 2021 • Sascha Hornauer, Ke Li, Stella X. Yu, Shabnam Ghaffarzadegan, Liu Ren
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet.
no code implementations • 1 Jan 2021 • Nanxiang Li, Shabnam Ghaffarzadegan, Liu Ren
We show both theoretically and experimentally, the VAE ensemble objective encourages the linear transformations connecting the VAEs to be trivial transformations, aligning the latent representations of different models to be "alike".
1 code implementation • 12 Jul 2020 • Bilal Alsallakh, Zhixin Yan, Shabnam Ghaffarzadegan, Zeng Dai, Liu Ren
We propose a measure to compute class similarity in large-scale classification based on prediction scores.
no code implementations • 27 Jan 2020 • Yiwei Sun, Shabnam Ghaffarzadegan
Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information.
no code implementations • ICLR 2020 • Nanxiang Li, Shabnam Ghaffarzadegan, Liu Ren
Recent advancements in unsupervised disentangled representation learning focus on extending the variational autoencoder (VAE) with an augmented objective function to balance the trade-off between disentanglement and reconstruction.
1 code implementation • 29 Sep 2019 • Ahmed Imtiaz Humayun, Shabnam Ghaffarzadegan, Md. Istiaq Ansari, Zhe Feng, Taufiq Hasan
Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases.
Signal Processing
no code implementations • 7 Aug 2019 • Bongjun Kim, Shabnam Ghaffarzadegan
The weakly labeled framework is used to eliminate the need for expensive data labeling procedure and self-supervised attention is deployed to help a model distinguish between relevant and irrelevant parts of a weakly labeled audio clip in a more effective manner compared to prior attention models.
no code implementations • 6 Jul 2018 • Shabnam Ghaffarzadegan
MIL is a weakly supervised learning problem where labels are associated with groups of instances (referred as bags) instead of individual instances.
1 code implementation • 18 Jun 2018 • Ahmed Imtiaz Humayun, Md. Tauhiduzzaman Khan, Shabnam Ghaffarzadegan, Zhe Feng, Taufiq Hasan
In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats SubChallenge.
1 code implementation • 15 Jun 2018 • Ahmed Imtiaz Humayun, Shabnam Ghaffarzadegan, Zhe Feng, Taufiq Hasan
In this work, we propound a novel CNN architecture that integrates the front-end bandpass filters within the network using time-convolution (tConv) layers, which enables the FIR filter-bank parameters to become learnable.