Abstract

Automatic identification of emotions from a given music track would be of significant importance in the area of Music Information Retrieval. Several studies have found lyrics an important feature in the task of emotion classification [1]. Using lyrics, till date, a transformer-model which is a Natural-Language-Processing (NLP) based Deep-Learning model has achieved state-of-the-art results in identifying music emotion as represented in the Valence-Arousal (VA) space [2]. While transformer models rely on parsing the syntactic structure of sequential input data, we assume that semantic relations could be efficiently learned from these representations using novel capsule networks [3]. The “capsules” are akin to neuronal populations breaking down information into parts that mimics human processing of information by chunking for efficient encoding and evaluation of the stimulus. We extend the same logic further to say that capsule networks should be able to look at the semantic representation of each word in the lyrics of a song and should be able to learn the relationship between these words and the emotion that they represent as a group

Abstract

Recent studies utilizing online music streaming data have found genre preferences to be associated with risk for depression (Surana et al., 2020). However, the mentioned study does not address gender differences among participants. Gender differences have been reported in depression in terms of the prevalence, symptomatology, and emotion regulation strategies used (Garnefski et al., 2004). Also, there exists a gender-specific tendency to choose certain kinds of music where females tend to prefer ‘softer’ styles like mainstream pop, while males are inclined towards ‘heavier’ styles like hard rock (North and Hargreaves, 2008). Martin et al.(1993) investigated the relationship between adolescents’ music preferences and risk factors for suicide, which included depression. Females preferring rock/metal music were found to have higher scores for depression as compared to males preferring rock/metal. Miranda and Claes (2009) found metal music-listening to be associated with higher depression levels in adolescent females, but only if they affiliate with peers who are more depressed. However, the above studies relied on self-reported music preferences. The current study explores these associations in more naturalistic settings using online music consumption.

Abstract

Personality is known to influence emotional experiences including those induced by music listening. Previous research has largely relied on self-report and models with limited terms, which may not adequately capture naturalistic behavior and experience. Data from the platform Last.fm allows for the analysis of listening behaviors using both musical features and semantic tags provided by listeners, for a more naturalistic exploration of personality, music and emotion.

Abstract

Studies have emerged characterizing naturally occurring music listening behaviour of individuals at-risk for depression via the online music streaming platform Last.fm (Surana et al. 2020a; Surana et al. 2020b), albeit limited to social tags and acoustic features. No studies have looked at artist preferences and predictive capacity using this information associated with risk for depression.

Abstract

Healthy-Unhealthy Music Scale (HUMS), a compact 13-item instrument was developed for assessing musical engagement that is indicative of adolescent well-being (Saarikallio et al, 2015, Child and Adolescent Mental Health). However, there have been no studies examining the validity of HUMS in a clinically depressed cohort (DC).

Abstract

The goal of this study is 1) to link an individual's personality traits to the number of instrumental music tracks in their long-term listening history 2) evaluate two datasets to assess reliability of results.

Abstract

This study aims to understand the relationship between personality traits and the emotions, as represented in the Valence-Arousal (VA) space, conveyed through lyrics of their preferred music.

Abstract

This study aims to first formulate a tool to identify an individual’s natural affinity towards appreciation of music vs lyrics and identify personality traits (including empathic and emotional traits) associated with the same.

Abstract

—Music has the power to evoke intense emotional experiences and regulate the mood of an individual. With the advent of online streaming services, research in music recommendation services has seen tremendous progress. Modern methods leveraging the listening histories of users for session-based song recommendations have overlooked the significance of features extracted from lyrics and acoustic content. We address the task of song prediction through multiple modalities, including tags, lyrics, and acoustic content. In this paper, we propose a novel deep learning approach by refining Attentive Neural Networks using representations derived via a Transformer model for lyrics and Variational Autoencoder for acoustic features. Our model achieves significant improvement in performance over existing state-of-the-art models using lyrical and acoustic features alone. Furthermore, we conduct a study to investigate the impact of users’ psychological health on our model’s performance. Index Terms—Music Recommend

Abstract

Music affects and in some cases reflects one’s emotional state. Key to this influence is lyrics and their meaning in conjunction with the acoustic properties of the track. Recent work has focused on analysing these acoustic properties and showing that individuals prone to depression primarily consume low valence and low energy music. However, no studies yet have explored lyrical content preferences in relation to online music consumptionofsuchindividuals. Inthecurrentstudy,weexamine lyrical simplicity, measured as the Compressibility and Absolute Information Content of the text, associated with preferencesofindividualsatriskfordepression. Usingthe six-month listening history of 541 Last.fm users, we compare lyrical simplicitytrendsforusersgroupedasbeingatrisk(At-Risk)ofdepression from those that are not (No-Risk). Our findings reveal that At-Risk individuals prefer songs with greater information content(lowerCompressibility)onaverage,especiallyforsongs characterised as Sad. Furthermore, we found that At-Risk individuals also have greater variability of Absolute Information Content across their listening history. We discuss the results in light of existing socio-psychological lab-based research on music habits associated with depression and their relevance to naturally occurring online music listening behaviour.