Studies which avoid these shortcomings are few and far between. 50% according to a review by Eerola and Vuoskoski ( 2011), and Juslin and Laukka ( 2003)). For the most part, this body of work has succeeded in determining the main musical elements that play such a role, however there are a number of shortcomings in a significant portion of the studies which either (a) deal with musical features in isolation (Dalla Bella, Peretz, Rousseau, & Gosselin, 2001 Ilie & Thompson, 2006), (b) are based on artificial stimulus materials (Bresin & Friberg, 2000 Gomez & Danuser, 2004 Vieillard et al., 2008), (c) are overly focused on the symbolic representation of music (Juslin, 1997b Gagnon & Peretz, 2000 Lindström, 2003), or (d) rely excessively on classical music (approx. It is therefore no surprise that there has been a wealth of research, over the past nine decades, into the individual elements of music that trigger certain emotions (e.g. Music itself could be considered as the single most important source for emotional communication in terms of all these separate factors. This process is undoubtedly complex, as it is related to aspects in the overall communication of emotions such as their perception or induction (Juslin & Västfjäll, 2008), models of emotions (Eerola & Vuoskoski, 2011), the personalities of listeners (Kallinen & Ravaja, 2004), and musical expectations (Huron, 2006). Music has the ability to convey powerful emotional meanings to listeners. In conclusion, the implications of the findings, and the genre-specificity of emotions in music are discussed. The most reliable musical features of affects across genres were identified, yielding aranked set of features most likely to operate across the genre. In contrast, the generalizability within genres was considerably higher (43% and 62% respectively), which suggests that emotions, especially those that express valence, operate differently depending on the musical genre. The models were fully validated across the datasets, suggesting low generalizability between the genres for valence (16% variance was accounted for) and moderately good generalizability between the genres for arousal (43%). Models were then constructed from theseto explain self-reports of valence and arousal, by using multiple andRandom Forest regression. Atotal of 39 musical features were extracted from the audio. Here this is investigated byanalysing nine separate datasets that represent categories ranging from classical (three sets), and film music (two), to popular music (two), and mixed genre (two).
However, no attempts have been made as yet to establish if there is a link between particular emotions and a specific genre. Empirical studies of emotions in music have described therole of individual musical features in recognizing particular emotions.
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