221 citations · 306 across the 21 of their papers we have counts for
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Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge
Florian Schmid, Paul Primus, Toni Heittola +4
This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a c…
Advancing Natural-Language Based Audio Retrieval with PaSST and Large Audio-Caption Data Sets
Paul Primus, Khaled Koutini, Gerhard Widmer
This work presents a text-to-audio-retrieval system based on pre-trained text and spectrogram transformers. Our method projects recordings and textual descriptions into a shared au…
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…
Receptive-field-regularized CNN variants for acoustic scene classification
Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer
Acoustic scene classification and related tasks have been dominated by Convolutional Neural Networks (CNNs). Top-performing CNNs use mainly audio spectograms as input and borrow th…
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification
Paul Primus, Hamid Eghbal-zadeh, David Eitelsebner +3
Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning. We study this problem in th…