Modeling Temporal Structure in Music for Emotion Prediction using Pairwise Comparisons

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2015

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The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level cognitive or semantic aspects of music such as emotions, genre, and similarity. This paper addresses the specific hypothesis whether temporal information is essential for predicting expressed emotions in music, as a prototypical example of a cognitive aspect of music. We propose to test this hypothesis using a novel processing pipeline: 1) Extracting audio features for each track resulting in a multivariate ”feature time series”. 2) Using generative models to represent these time series (acquiring a complete track representation). Specifically, we explore the Gaussian Mixture model, Vector Quantization, Autoregressive model, Markov and Hidden Markov models. 3) Utilizing the generative models in a discriminative setting by selecting the Probability Product Kernel as the natural kernel for all considered track representations. We evaluate the representations using a kernel based model specifically extended to support the robust two-alternative forced choice self-report paradigm, used for eliciting expressed emotions in music. The methods are evaluated using two data sets and show increased predictive performance using temporal information, thus supporting the overall hypothesis.
Original languageEnglish
Title of host publicationProceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014)
Number of pages6
PublisherInternational Society for Music Information Retrieval
Publication date2014
Pages319-324
StatePublished - 2014
Event15th International Society for Music Information Retrieval Conference (ISMIR 2014) - Taipei, Taiwan, Province of China

Conference

Conference15th International Society for Music Information Retrieval Conference (ISMIR 2014)
Number15
CountryTaiwan, Province of China
CityTaipei
Period27/10/201431/10/2014
Internet address

Bibliographical note

copyright: Jens Madsen, Bjørn Sand Jensen, Jan Larsen. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Jens Madsen, Bjørn Sand Jensen, Jan Larsen. “Modeling Temporal Structure in Music for Emotion Prediction using Pairwise Comparisons”, 15th International Society for Music Information Retrieval Conference, 2014.

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