9 citations · 48 across the 9 of their papers we have counts for
5 papers · 1 filter
audioLIME: Listenable Explanations Using Source Separation
Verena Haunschmid, Ethan Manilow, Gerhard Widmer
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable. We propose au…
Towards Musically Meaningful Explanations Using Source Separation
Verena Haunschmid, Ethan Manilow, Gerhard Widmer
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks. Such models are usually considered "black boxes", meaning that th…
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…
Towards Explainable Music Emotion Recognition: The Route via Mid-level Features
Shreyan Chowdhury, Andreu Vall, Verena Haunschmid +1
Emotional aspects play an important part in our interaction with music. However, modelling these aspects in MIR systems have been notoriously challenging since emotion is an inhere…
Two-level Explanations in Music Emotion Recognition
Verena Haunschmid, Shreyan Chowdhury, Gerhard Widmer
Current ML models for music emotion recognition, while generally working quite well, do not give meaningful or intuitive explanations for their predictions. In this work, we propos…