Abstract We present a platform and a dataset to help research on Music Emotion Recognition (MER). We developed the Music Enthusiasts platform aiming to improve the gathering and analysis of the so-called “ground truth” needed as input to MER systems. Firstly, our platform involves engaging participants using citizen science strategies and generate music emotion annotations – the platform presents didactic information and musical recommendations as incentivization, and collects data regarding demographics, mood, and language from each participant. Participants annotated each music excerpt with single free-text emotion words (in native language), distinct forced-choice emotion categories, preference, and familiarity. Additionally, participants stated the reasons for each annotation – including those distinctive of emotion perception and emotion induction. Secondly, our dataset was created for personalized MER and contains information from 181 participants, 4721 annotations, and 1161 music excerpts. To showcase the use of the dataset, we present a methodology for personalization of MER models based on active learning. The experiments show evidence that using the judgment of the crowd as prior knowledge for active learning allows for more effective personalization of MER systems for this particular dataset. Our dataset is publicly available and we invite researchers to use it for testing MER systems. 

/pdf/trompa-mer-an-open-dataset-for-personalized-music-emotion-vttukqef.pdf

TROMPA-MER: an open dataset for personalized music emotion recognition

A high quality and large scale training data-set is an important guarantee to teach an ideal classifier for image classification. Manually constructing a training data- set  with  appropriate  labe ...

Enhancing Deep Active Learning Using Selective Self-Training For Image Classification

Abstract We present a platform and a dataset to help research on Music Emotion Recognition (MER). We developed the Music Enthusiasts platform aiming to improve the gathering and analysis of the so-called “ground truth” needed as input to MER systems. Firstly, our platform involves engaging participants using citizen science strategies and generate music emotion annotations -- the platform presents didactic information and musical recommendations as incentivization, and collects data regarding demographics, mood, and language from each participant. Participants annotated each music excerpt with single free-text emotion words (in native language), distinct forced-choice emotion categories, preference, and familiarity. Additionally, participants stated the reasons for each annotation -- including those distinctive of emotion perception and emotion induction. Secondly, our dataset was created for personalized MER and contains information from 181 participants, 4721 annotations, and 1161 music excerpts. To showcase the use of the dataset, we present a methodology for personalization of MER models based on active learning. The experiments show evidence that using the judgment of the crowd as prior knowledge for active learning allows for more effective personalization of MER systems for this particular dataset. Our dataset is publicly available and we invite researchers to use it for testing MER systems. 

/pdf/trompa-mer-an-open-dataset-for-personalized-music-emotion-2bfsho3k.pdf

TROMPA-MER: an open dataset for personalized Music Emotion Recognition

Previous research in music emotion recognition (MER) has tackled the inherent problem of subjectivity through the use of personalized models – models which predict the emotions that a particular user would perceive from music. Personalized models are trained in a supervised manner, and are tested exclusively with the annotations provided by a specific user. While past research has focused on model adaptation or reducing the amount of annotations required from a given user, we propose a methodology based on uncertainty sampling and query-by-committee, adopting prior knowledge from the agreement of human annotations as an oracle for active learning (AL). We assume that our disagreements define our personal opinions and should be considered for personalization. We use the DEAM dataset, the current benchmark dataset for MER, to pre-train our models. We then use the AMG1608 dataset, the largest MER dataset containing multiple annotations per musical excerpt, to re-train diverse machine learning models using AL and evaluate personalization. Our results suggest that our methodology can be beneficial to produce personalized classification models that exhibit different results depending on the algorithms’ complexity.

Let's agree to disagree: Consensus Entropy Active Learning for Personalized Music Emotion Recognition

We consider the problem of active learning over the vertices in a graph, without feature representation. Our study is based on the common graph smoothness assumption, which is formulated in a Gaussian random field model. We analyze the probability distribution over the unlabeled vertices conditioned on the label information, which is a multivariate normal with the mean being the harmonic solution over the field. Then we select the nodes to label such that the total variance of the distribution on the unlabeled data, as well as the expected prediction error, is minimized. In this way, the classifier we obtain is theoretically more robust. Compared with existing methods, our algorithm has the advantage of selecting data in a batch offline mode with solid theoretical support. We show improved performance over existing label selection criteria on several real world data sets.

A variance minimization criterion to active learning on graphs

In recent decades, the availability of a large amount of data has propelled the field of machine learning enormously. Machine learning, however, relies heavily on the availability of annotated data, typically labels indicating to which class a data instance belongs. With the huge amounts of data, this raises the question of how to efficiently annotate data, certainly when having limited resources. This thesis addresses the particular challenge of using as few annotations as possible, while at the same time, maintaining a good learning performance. For that we utilize active learning, which iteratively chooses the most valuable instances as to obtain the labels froman oracle (e.g. a human expert). Though many studies have demonstrated that active learning can reduce the annotation cost, there are still several issues that limit its practical use. This thesis makes a further step forwards making active learning more practical for real-world applications. We first provide a benchmark and comparison of six different categories of active learning algorithms built on logistic regression. This work provides a better understanding of the underlying characteristics of various active learners and illustrates the potential benefits of using such techniques, but it also provides many cases for which active learning fails to outperform passive learning (i.e. randomly selecting instances for labeling). Those failed cases motivate us to propose two novel active learning methods that show a clear advantage over passive learning. The first one proposes to weight the so-called retraining-based criteria with an uncertainty score that is measured by the estimated posterior probability. The second one measures the usefulness of unlabeled instances according to the variance of the predictive probability. This method takes an additional step towards practical active learning, clearly outperforming current state of the art on binary andmulti-class classification tasks. We further consider two realistic issues when applying active learning to real-world problems. One is how to find an initial set that contains at least one instance per class to start the active labeling cycle. The other one is dealing with the absence of human annotators in the interactive labeling loop. We propose new approaches to tackle the above problems and observe good performance compared to existing methods. This thesis concludes with an analysis of the contributions and limitations of our work, as well as research directions that deserve further studies. We hope that this thesis also inspires others to make active learning more suitable for real-world applications.

https://repository.tudelft.nl/islandora/object/uuid%3A12720d56-8a35-4b36-b287-2e301ae69bd0/datastream/OBJ/download

Towards Practical Active Learning for Classification

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Towards Practical Active Learning for Classification