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20172021
most citedReceptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks

55 citations · 94 across the 10 of their papers we have counts for

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cs.SD2021

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…

cs.SD202155 cited

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…

cs.SD20197 cited

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…

cs.SD2018

Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments

Romain Serizel, Nicolas Turpault, Hamid Eghbal-Zadeh +1

This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries). The target of t…

cs.SD2017

A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification

Hamid Eghbal-zadeh, Bernhard Lehner, Matthias Dorfer +1

In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the oth…