55 citations · 79 across the 7 of their papers we have counts for
10 papers
Over-Parameterization and Generalization in Audio Classification
Khaled Koutini, Hamid Eghbal-zadeh, Florian Henkel +2
Convolutional Neural Networks (CNNs) have been dominating classification tasks in various domains, such as machine vision, machine listening, and natural language processing. In ma…
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks
Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer
In this paper, we study the performance of variants of well-known Convolutional Neural Network (CNN) architectures on different audio tasks. We show that tuning the Receptive Field…
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping
Khaled Koutini, Florian Henkel, Hamid Eghbal-zadeh +1
Deep Neural Networks are known to be very demanding in terms of computing and memory requirements. Due to the ever increasing use of embedded systems and mobile devices with a limi…
Receptive-Field Regularized CNNs for Music Classification and Tagging
Khaled Koutini, Hamid Eghbal-Zadeh, Verena Haunschmid +3
Convolutional Neural Networks (CNNs) have been successfully used in various Music Information Retrieval (MIR) tasks, both as end-to-end models and as feature extractors for more co…
On Data Augmentation and Adversarial Risk: An Empirical Analysis
Hamid Eghbal-zadeh, Khaled Koutini, Paul Primus +5
Data augmentation techniques have become standard practice in deep learning, as it has been shown to greatly improve the generalisation abilities of models. These techniques rely o…
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs
Khaled Koutini, Shreyan Chowdhury, Verena Haunschmid +2
We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels. We perform an invest…