We have developed a method to obtain a reliable “ground truth” database for automatic music mood classification.

Tracks with high valence sound more positive (e.g., happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g., sad, depressed, angry).

This work has developed a method to obtain a reliable “ground truth” database for automatic music mood classification and confirms that excerpt selection is ...

In this study, we explore the viability of crowdsourcing music mood classification judgments using Amazon Mechanical Turk (MTurk). Specifically, we compare the ...

Ground truth for automatic music mood classification ; Volume: ISMIR 2006, 7th International Conference on Music Information Retrieval, Victoria, Canada, 8-12 ...

The method is based on results from psychological studies and framed into a supervised learning approach using musical features automatically extracted from the ...

However, the ground truth values for amplitude-based features limited the performance of the model using the Kokborok dataset. Medina [13] developed an ...

data (called ground truth) to classify new instances with the highest accuracy ... A ground-truth set of 600 tracks distributed across five mood cate- gories ...

Mood classification is one of a variation of the audio classification however has even more elusive ground truth [2, 3] . In mood classification, not only ...

A K-means clustering method is applied to create a simple yet meaningful cluster-based set of high-level mood categories as well as a ground-truth dataset ...