EMO100DB: An Open Dataset of Improvised Songs with Emotion Data

TL;DR

This paper introduces Emo100DB, a comprehensive dataset of improvised songs with associated emotion data, including lyrics, MIDI, and audio, to facilitate research on music and emotion analysis.

Contribution

The creation of Emo100DB, a novel dataset combining improvised music recordings with emotion annotations based on Russell's circumplex model, is the key contribution.

Findings

Dataset includes 20 participants' improvised songs with emotion labels.

Contains lyrics, MIDI files, and original WAV recordings.

Organized into four emotion quadrants for analysis.

Abstract

In this study, we introduce Emo100DB: a dataset consisting of improvised songs that were recorded and transcribed with emotion data based on Russell's circumplex model of emotion. The dataset was developed by collecting improvised songs that consist of melody, lyrics, and an instrumental accompaniment played, sung, and recorded by 20 young adults. Before recording each song, the participants were asked to report their emotional state, with the axes representing arousal and valence based on Russell's circumplex model of emotions. The dataset is organized into four emotion quadrants, and it includes the lyrics text and MIDI file of the melody extracted from the participant recordings, along with the original audio in WAV format. By providing an integrated composition of data and analysis, this study aims to offer a comprehensive dataset that allows for a diverse exploration of the relationship between music and emotion.