![]() 1, the expert thought that this emotion was related to the High Negative Affect.Īmor (Love): The concept of love used in this work is based on the concept from the Trovadorism (XII Century) until the Romanticism period (XVIII and XIX) will be used as basis for classification. It is similar to repentance, but differs from it in that repentance is focused on the choices people have that lead to a negative result, while disappointment is focused on the result itself. 1, the expert thought that this emotion was related to Unpleasantness.ĭecepção (Disappointment): Feeling of dissatisfaction that arises when expectations about something or someone do not come to fruition. Sadness may be the result of emotions like selfishness, insecurity, low self-esteem, jealousy, immaturity, fear, and disillusionment. Discouragement or frustration towards someone or something. Tristeza (Sadness): grief, melancholy, sorrow, or distress. In total, the expert assigned one of the following six emotions to each song: An additional reason for asking the help of an expert is that the LMD is one of the few music databases that had its music genre labels assigned by experts and we wanted to keep the LMD that way (i.e., a database whose labels have always been assigned by experts). The expert also felt that these emotions are reminiscent of the emotional plane provided by the Affective Circumference Model of Watson and Telegen (presented in Fig. After these meetings, the expert suggested that six emotions could be used to map the predominant emotion encoded in each song of the LMD. In order to label the songs, we had a few meetings with the expert where we discussed the current labelling approaches used in the related works. It should be noted that the expert is an acoustic guitar player who is also fluent in both Portuguese and Spanish languages. ![]() As we shall see in Section 4, there is no consensus among the Music Information Retrieval community about which approach should be used, or, in the case of categorical labels, how many labels and which labels should be used.įor these reasons, in this work, we asked the help of a professionally trained Brazilian Musician to help us with the assignment of mood labels to each song in the LMD, creating the Latin Music Mood Database. Some of the previous work have used categorical labels while others have used emotions based on emotional plane. The second challenge when creating a novel (or applying new labels to an existing database, as in our case) is which approach to use to conceptualise the concept of emotion in music. In this work, we have chosen to label the predominant emotion perceived in each song of the Latin Music Database. In the case of music emotion classification, the ground-truth labels can be related to the emotions perceived by listening to the songs or evoked by listening to the songs. The first challenge when creating a novel emotion or mood database is what to label as ground-truth. In Section 4, we present the related work, and in Section 5, we present the conclusions of this work. ![]() In Section 3 we present a data analysis of this novel database. The process of assigning mood labels is presented in Section 2. ![]() The main contribution of this paper is to present the Latin Music Mood Database, which is an extension of the LMD where each song in the LMD has one mood label associated with it. One of the main differences between the LMD and other databases is that the genre labels were assigned to each song in the database by two ballroom and Brazilian cultural dances teachers with over ten years of experience. The LMD was originally developed for the task of automatic music genre classification and contains 3136 songs from ten different Latin music genres. In this paper, instead of creating a novel database and using one of the previous approaches to label it with emotion labels, we decided to apply emotion/mood labels to an already existing database, namely the Latin Music Database (LMD). ![]() There has also been some efforts to automate and semi-automate the mood assignment processes. Some of the approaches used so far vary from the manual assignment of categorical emotion labels to user-listening tests. Over the years, several approaches have been used to label different music databases with emotion/mood labels. Within the Music Information Retrieval (MIR) community, one particular task that has become increasingly popular is the task of music emotion (or mood) classification. ![]()
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