9 citations · 36 across the 12 of their papers we have counts for
12 papers
DExter: Learning and Controlling Performance Expression with Diffusion Models
Huan Zhang, Shreyan Chowdhury, Carlos Eduardo Cancino-Chacón +3
In the pursuit of developing expressive music performance models using artificial intelligence, this paper introduces DExter, a new approach leveraging diffusion probabilistic mode…
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings
Shreyan Chowdhury, Gerhard Widmer
This paper explores a specific sub-task of cross-modal music retrieval. We consider the delicate task of retrieving a performance or rendition of a musical piece based on a descrip…
Are we describing the same sound? An analysis of word embedding spaces of expressive piano performance
Silvan David Peter, Shreyan Chowdhury, Carlos Eduardo Cancino-Chacón +1
Semantic embeddings play a crucial role in natural language-based information retrieval. Embedding models represent words and contexts as vectors whose spatial configuration is der…
Decoding and Visualising Intended Emotion in an Expressive Piano Performance
Shreyan Chowdhury, Gerhard Widmer
Expert musicians can mould a musical piece to convey specific emotions that they intend to communicate. In this paper, we place a mid-level features based music emotion model in th…
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…
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…
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…
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…
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…
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…