The field of music and emotion research has grown rapidly and diversified during the last decade. This has led to a certain degree of confusion and inconsistency between competing notions of emotions, data, and results. The present review of 251 studies describes the focus of prevalent research approaches, methods, and models of emotion, and documents the types of musical stimuli used over the past twenty years. Although self-report approaches to emotions are the most common way of dealing with music and emotions, using multiple approaches is becoming increasingly popular. A large majority (70%) of the studies employed variants of the discrete or the dimensional emotion models. A large proportion of stimuli rely on a relatively modest amount of familiar classical examples. The evident shortcomings of these prevalent patterns in music and emotion studies are highlighted, and concrete plans of action for future studies are suggested.

considerable effort has been made towards understanding how acoustic and structural features contribute to emotional expression in music, but relatively little attention has been paid to the role of timbre in this process. Our aim was to investigate the role of timbre in the perception of affect dimensions in isolated musical sounds, by way of three behavioral experiments. In Experiment 1, participants evaluated perceived affects of 110 instrument sounds that were equal in duration, pitch, and dynamics using a three-dimensional affect model (valence, energy arousal, and tension arousal) and preference and emotional intensity. In Experiment 2, an emotional dissimilarity task was applied to a subset of the instrument sounds used in Experiment 1 to better reveal the underlying affect structure. In Experiment 3, the perceived affect dimensions as well as preference and intensity of a new set of 105 instrument sounds were rated by participants. These sounds were also uniform in pitch, duration, and playback dynamics but contained systematic manipulations in the dynamics of sound production, articulation, and ratio of high-frequency to low-frequency energy. The affect dimensions for all the experiments were then explained in terms of the three kinds of acoustic features extracted: spectral (e.g., ratio of high-frequency to low-frequency energy), temporal (e.g., attack slope), and spectro-temporal (e.g., spectral flux). High agreement among the participants' ratings across the experiments suggested that even isolated instrument sounds contain cues that indicate affective expression, and these are recognized as such by the listeners. A dominant portion (50-57%) of the two dimensions of affect (valence and energy arousal) could be predicted by linear combinations of few acoustic features such as ratio of high-frequency to low-frequency energy, attack slope, and spectral regularity. Links between these features and those observed in the vocal expression of affects and other sound phenomena are discussed.

although people generally avoid negative emotional experiences in general, they often enjoy sadness portrayed in music and other arts. The present study investigated what kinds of subjective emotional experiences are induced in listeners by sad music, and whether the tendency to enjoy sad music is associated with particular personality traits. One hundred forty-eight participants listened to 16 music excerpts and rated their emotional responses. As expected, sadness was the most salient emotion experienced in response to sad excerpts. However, other more positive and complex emotions such as nostalgia, peacefulness, and wonder were also evident. Furthermore, two personality traits – Openness to Experience and Empathy – were associated with liking for sad music and with the intensity of emotional responses induced by sad music, suggesting that aesthetic appreciation and empathetic engagement play a role in the enjoyment of sad music.

AN OVERVIEW OF THE MAIN INSTRUMENT SAMPLE libraries used in psychoacoustics, sound analysis, and instrument classification research is presented. One of the central libraries, the McGill University Master Samples (MUMS, Opolko & Wapnick, 2006) is reviewed in detail. This library has over 6000 sound samples representing most classical and popular musical instruments and a wide variety of articulation styles.A closer scrutiny revealed a conspicuous amount of labeling errors, intonation inaccuracies, and the absence of an integrated database. These errors are identified and catalogued, and revisions are implemented in a form of an installer.

Listeners are sensitive to pitch distributional information in music (N. Oram & L. L. Cuddy, 1995; C. L. Krumhansl, J. Louhivuori, P.Toiviainen, T. Jäärvinen, & T. Eerola, 1999). However, it is uncertain whether frequency-based musical features are sufficient to explain the similarity judgments that underlie listeners' classification processes. A similarity rating experiment was designed to determine the effectiveness of these features in predicting listeners' similarity ratings. The material consisted of 15 melodies representing five folk music styles. A multiple regression analysis showed that the similarity of frequency-based musical properties could account for a moderate amount (40%%) of listeners' similarity ratings. A slightly better predictive rate (55%%) was achieved by using descriptive variables such as number of tones, rhythmic variability, and melodic predictability. The results suggest that both measures were able to capture some aspects of the structures that portray common salient dimensions to which listeners pay attention while categorizing melodies. Aikaisemmissa tutkimuksissa on osoitettu, ettää musiikin tilastollisilla tapahtumilla, kuten säävelten määäärillää ja tyypillisillää intervalleilla, on merkitystää, kun kuulijat muodostavat kääsityksiääään musiikin rakenteesta (N. Oram & L. L. Cuddy, 1995; C. L. Krumhansl, J. Louhivuori, P. Toiviainen, T. Jäärvinen, & T. Eerola, 1999). Nääiden piirteiden voidaan olettaa olevan täärkeitää myöös musiikin luokittelussa. Toistaiseksi ei kuitenkaan tiedetää, miten hyvin tilastollisilla piirteillää voitaisiin musiikin luokittelua selittääää. Täätää testattiin kuulijoille jäärjestetyn samanlaisuusarviointitehtäävään avulla. Tutkimuksen materiaali koostui 15 melodiasta, jotka edustivat viittää eri kansanmusiikkityyliää. Regressioanalyysi paljasti, ettää musiikin tilastollisten piirteiden samanlaisuus pystyi selittäämääään kohtuullisen määäärään (40%%) kuulijoiden antamista samanlaisuusarvioista. Hieman parempi selitysaste (55%%) saavutettiin kuvaavilla muuttujilla, joita olivat melodian laajuus ja ennakoitavuus sekää rytmin vaihtelevuus. Nääin ollen tulokset antavat aiheen olettaa, ettää musiikin tilastolliset piirteet ja kuvailevat muuttujat vaikuttavat kuulijoiden luokittelupäääätööksiin.

This study of Finnish spiritual folk hymns combined three approaches to understanding melodic expectation. The first approach was a statistical style analysis of a representative corpus of 18 hymns, which determined the relative frequencies of tone onsets and two- and three-tone transitions. The second approach was a behavioral experiment in which listeners, either familiar (experts) or unfamiliar (nonexperts) with the hymns, made judgments about melodic continuations. The third approach simulated melodic expectation with neural network models of the self-organizing map (SOM) type (Kohonen, 1997). One model was trained on a corpus of Finnish folk songs and Lutheran hymns (Finnish SOM), while another was trained with the hymn contexts used in the experiment with the correct continuation tone (Hymn SOM). The three approaches converged on the following conclusions: (1) Listeners appear to be sensitive to the distributions of tones and tone transitions in music, (2) The nonexperts' responses more strongly reflected the general distribution of tones, whereas the experts' responses more strongly reflected the tone transitions and the correct continuations, (3) The SOMs produced results similar to listeners and also appeared sensitive to the distributions of tones and tone transitions, (4) The Hymn SOM correlated more strongly with the experts' judgments than the Finnish SOM, and (5) the principles of the implication-realization model (Narmour, 1990) were weighted similarly by the behavioral data and the Hymn SOM. /// Tässä suomalaisia hengellisiä kansansävelmiä käsittelevässä tutkimuksessa pyrittiin selvittämään melodisia odotuksia kolmen tutkimusmenetelmän avulla. Ensimmäinen menetelmä oli kyseistä tyyliä edustavien 18 sävelmän tilastollinen analyysi, jossa määritelteltiin sävelkorkeuksien sekä kahden ja kolmen sävelen siirtymien tilastolliset jakaumat. Toinen menetelmä oli behavioraalinen koe, jossa kuulijat arvioivat sävelmien jatkoja. Kuulijat jakaantuivat kahteen ryhmään: sävelmät tunteviin (asiantuntijoihin) ja sävelmiä tuntemattomiin (ei-asiantuntijoihin). Kolmannessa menetelmässä simuloitiin melodisia odotuksia itsejärjestäytyvään karttaan (Kohonen, 1997) perustuvalla keinotekoisella hermoverkkomallilla. Ensimmäiselle mallille opetettiin joukko suomalaisia kansanlauluja ja luterilaisia virsiä (suomalainen verkko), toiselle kokeessa käytettyjä hengellisiä kansansävelmiä (hengellinen verkko). Käytetyt menetelmät tuottivat yhteneviä tuloksia ja antoivat aihetta seuraaviin johtopäätöksiin: (1) kuulijat näyttävät olevan vastaanottavaisia musiikin säveljakaumille ja sävelsiirtymille, (2) ei-asiantuntijoiden vastaukset noudattivat enemmän sävelten yleistä jakaumaa, kun taas asiantuntijoiden vastaukset heijastivat enemmän sävelsiirtymiä ja sävelmien oikeita jatkoja, (3) hermoverkot tuottivat tuloksia, jotka olivat samankaltaisia kuulijoiden arvioiden kanssa ja jotka noudattivat sävelten ja sävelsiirtymien jakaumia, (4) hengellisen verkon tulokset korreloivat suomalaisen verkon tuloksia voimakkaammin asiantuntijoiden arvioiden kanssa, ja (5) behavioraaliset tulokset ja hengellinen verkko painottavat implikaatio-realisaatio-mallin (Narmour, 1990) periaatteita samalla tavalla.