activity
20192021
most citedTowards Explainable Music Emotion Recognition: The Route via Mid-level Features

9 citations · 36 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SD20214 cited

On Perceived Emotion in Expressive Piano Performance: Further Experimental Evidence for the Relevance of Mid-level Perceptual Features

Shreyan Chowdhury, Gerhard Widmer

Despite recent advances in audio content-based music emotion recognition, a question that remains to be explored is whether an algorithm can reliably discern emotional or expressiv…

cs.SD20216 cited

Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities

Shreyan Chowdhury, Verena Praher, Gerhard Widmer

Music emotion recognition is an important task in MIR (Music Information Retrieval) research. Owing to factors like the subjective nature of the task and the variation of emotional…

cs.SD2021

Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation

Shreyan Chowdhury, Gerhard Widmer

Emotion and expressivity in music have been topics of considerable interest in the field of music information retrieval. In recent years, mid-level perceptual features have been su…

cs.SD20203 cited

On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game

Carlos Cancino-Chacón, Silvan Peter, Shreyan Chowdhury +2

A piece of music can be expressively performed, or interpreted, in a variety of ways. With the help of an online questionnaire, the Con Espressione Game, we collected some 1,500 de…

eess.AS20202 cited

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…

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…