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Found 9 papers, 4 papers with code
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings
On the text side, we use emotion-enriched word embeddings (EWE) and on the audio side, we extract mid-level perceptual features instead of generic audio embeddings.
Are we describing the same sound? An analysis of word embedding spaces of expressive piano performance
Using a music research dataset of free text performance characterizations and a follow-up study sorting the annotations into clusters, we derive a ground truth for a domain-specific semantic similarity structure.
Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities
In previous work, we have shown how to derive explanations of model predictions in terms of spectrogram image segments that connect to the high-level emotion prediction via a layer of easily interpretable perceptual features.
Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation
Emotion and expressivity in music have been topics of considerable interest in the field of music information retrieval.
On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game
In this paper, we offer a first account of this new data resource for expressive performance research, and provide an exploratory analysis, addressing three main questions: (1) how similarly do different listeners describe a performance of a piece?
Receptive-Field Regularized CNNs for Music Classification and Tagging
However, the MIR field is still dominated by the classical VGG-based CNN architecture variants, often in combination with more complex modules such as attention, and/or techniques such as pre-training on large datasets.
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs
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.
Towards Explainable Music Emotion Recognition: The Route via Mid-level Features
Emotional aspects play an important part in our interaction with music.
Two-level Explanations in Music Emotion Recognition
Current ML models for music emotion recognition, while generally working quite well, do not give meaningful or intuitive explanations for their predictions.
