MERP: A Music Dataset with Emotion Ratings and Raters’ Profile Information

Abstract

Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence-arousal space. Four additional songs were selected from the DEAM dataset to act as a benchmark in this study and filter out low quality ratings. A total of 452 participants participated in annotating the dataset, with 277 participants remaining after thoroughly cleaning the dataset. Their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.

Construction of a Chinese traditional instrumental music dataset: A validated set of naturalistic affective music excerpts

“…As shown, both the dimensional (Belfi & Kacirek, 2021 ; Koh et al, 2022 ; Li et al, 2012 ; Lepping et al, 2016 ; Imbir & Gołąb, 2017 ; Rainsford et al, 2018 ) and discrete (Hill and Palmer, 2010 ; Xu et al, 2017 ; Vieillard et al, 2008 ) emotion models have been employed to categorize music emotion, yet only a few studies have employed both models for music classification (Eerola & Vuoskoski, 2011 ; Xie & Gao, 2022 ). Most of the datasets use Western music and pop music (Belfi & Kacirek, 2021 ; Eerola & Vuoskoski, 2011 ; Imbir & Gołąb, 2017 ; Koh et al, 2022 ; Li et al, 2012 ; Lepping et al, 2016 ), and only three studies utilize Chinese traditional instrumental music (Li et al, 2012 ; Xie & Gao, 2022 ; Xu et al, 2017 ). Li et al ( 2012 ) and Xu et al ( 2017 ) constructed emotional music datasets containing Chinese traditional instrumental music.…”

Multimodal Music Datasets? Challenges and Future Goals in Music Processing

“…Multimodal datasets used in emotion-centric tasks, such as CAL500 (38), and AMC (39), combine audio features with emotion annotations. Additional datasets, including those from (40)(41)(42)(43)(44)(45), incorporate labels, lyrics, and participant information. Integrating lyrics with audio data provides additional context, enhancing emotion recognition accuracy.…”

Navigating Challenges in Multimodal Music Data Management for AI Systems

“…Multimodal music datasets usually contain personal information, like physiological measurements (24) or users' profile information (25). The protection of this data and the handling of it securely and confidentially belongs to the category of data privacy.…”