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A number of different cues allow listeners to perceive musical meter. Three experiments examined effects of melodic and temporal accents on perceived meter in excerpts from folk songs scored in 6/8 or 3/4 meter. Participants matched excerpts with 1 of 2 metrical drum accompaniments. Melodic accents included contour change, melodic leaps, registral extreme, melodic repetition, and harmonic rhythm. Two experiments with isochronous melodies showed that contour change and melodic repetition predicted judgments. For longer melodies in the 2nd experiment, variables predicted judgments best at the beginning of excerpts. The final experiment, with rhythmically varied melodies, showed that temporal accents, tempo, and contour change were the strongest predictors of meter. The authors' findings suggest that listeners combine multiple melodic and temporal features to perceive musical meter.

The Finnish Centre of Excellence is an interdisciplinary research consortium funded by the Academy of Finland (2008 -2013) that explores the relationships between physical, perceptual, emotional, and social aspects of musical engagement. The research themes of the Centre consist broadly of emotions, movement, rehabilitation, and learning, all with respect to music. Within these topic areas, particular emphasis is given to perception of musical elements such as rhythm, melody, and timbre (including their neural representations); improvement of language learning; effectiveness of music therapy and various music interventions; development of computational analysis tools for music research; aesthetic experi- ences of music; music-induced movement (including rhythmic synchronization); emotion expression; and motor communication.Supplemental materials: http://dx.doi.org/10.1037/a0031008.suppAim of the CentreThe Finnish Centre of Excellence is an interdisciplinary re- search consortium funded by the Academy of Finland (2008 - 2013) that explores the relationships between physical, perceptual, emotional, and social aspects of musical engagement. The research themes of the Centre consist broadly of emotions, movement, rehabilitation, and learning, all with respect to music. Within these topic areas, particular emphasis is given to perception of musical elements such as rhythm, melody, and timbre (including their neural representations); improvement of language learning; effec- tiveness of music therapy and various music interventions; devel- opment of computational analysis tools for music research; aes- thetic experiences of music; music-induced movement (including rhythmic synchronization); emotion expression; and motor com- munication.The aim of the Centre is to achieve a critical mass of expertise that is able to combine different methods, approaches, and disci- plines in tackling the central research themes. The Centre com- bines theoretical backgrounds and methods from the fields and subfields of musicology, music therapy, psychology, movement science, music information retrieval, and cognitive neuroscience. The scope of methods used in the Centre is also wide and includes computational music analysis, behavioral experimentation, optical motion capture, various neuroimaging techniques, and longitudinal studies.Main Scientific ContributionsDuring the past 4 years, the Centre has produced 280 publica- tions, including 250 articles in refereed international publications. The publication forums include journals such as Brain, Cortex, British Journal of Psychiatry, Journal of Cognitive Neuroscience, NeuroImage, Neuropsychologia, Music Perception, PLoS One, PNAS, Psychology of Music, and Psychomusicology: Music, Mind & Brain. This prolific publication activity has also been docu- mented in a recent bibliographical study on articles published in Music Perception (Tirovolas & Levitin &, 2011), which ranked Finland as the most productive country per capita in this journal, largely driven by members of the Centre. The most important research findings so far include the following: (1) regular music listening enhances recovery from stroke (Sarkamo et al., 2008); (2) music therapy alleviates depression (Erkkila et al., 2011); (3) infants are predisposed to move to the rhythm of music (Zentner & Eerola, 2010); (4) dance moves reveal personality traits (Luck, Saarikallio, Burger, Thompson, & Toiviainen, 2010); (5) first documented case of a new form of amusia, beat deafness (Phillips- Silver et al., 2011); (6) musical aptitude and training is linked to better second-language linguistic abilities (Milovanov & Tervani- emi, 2011); (7) brain encoding differs between sad and happy music as well as between instrumental music and songs (Brattico et al., 2011).The most important methodological innovations to date include the following: (1) a new functional Magnetic Resonance Imaging (fMRI) paradigm for studying neural correlates in a naturalistic listening condition (Alluri et al. …

The problem of tracking a hand in video has gained a lot of attention due to its numerous applications in human computer interfaces. So far, the work has been limited to the use of standard speed videos, but the recent developments in imaging technology and computing hardware have made it attractive to exploit high-speed imaging for tracking the hand more accurately both in space and time. To produce videos of good quality, the high-speed imaging requires more light when compared to imaging with conventional frame rates. Therefore, grey-scale high-speed imaging is in common use and this makes the use of hand tracking methods relying specifically on color information unsuitable. In this work, we provide the first solid comparison of state-of-the-art general object trackers on hand tracking with a primary focus on grey-scale high-speed videos. Novel annotated high-speed video data were collected and made publicly available for evaluation purposes. The algorithms were tested with both finger and hand targets, and with grey-scale and color videos. In addition to tracking accuracies, the stability, sensitivity, and the processing speeds of the algorithms were evaluated. The experiments show that the results vary significantly in all aspects, but certain methods such as Compressive Tracking and Hough Track methods performed better overall.

This article is a study of melodic expectancy in North Sami yoiks, a style of music quite distinct from Western tonal music. Three different approaches were taken. The first approach was a statistical style analysis of tones in a representative corpus of 18 yoiks. The analysis determined the relative frequencies of tone onsets and two- and three-tone transitions. It also identified style characteristics, such as pentatonic orientation, the presence of two reference pitches, the frequency of large consonant intervals, and a relatively large set of possible melodic continuations. The second approach was a behavioral experiment in which listeners made judgments about melodic continuations. Three groups of listeners participated. One group was from the Sami culture, the second group consisted of Finnish music students who had learned some yoiks, and the third group consisted of Western musicians unfamiliar with yoiks. Expertise was associated with stronger veridical expectations (for the correct next tone) than schematic expectations (based on general style characteristics). Familiarity with the particular yoiks was found to compensate for lack of experience with the musical culture. The third approach simulated melodic expectancy with neural network models of the self-organizing map (SOM) type (Kohonen, T. (1997). Self-organizing maps (2nd ed.). Berlin: Springer). One model was trained on the excerpts of yoiks used in the behavioral experiment including the correct continuation tone, while another was trained with a set of Finnish folk songs and Lutheran hymns. The convergence of the three approaches showed that both listeners and the SOM model are influenced by the statistical distributions of tones and tone sequences. The listeners and SOM models also provided evidence supporting a core set of psychological principles underlying melody formation whose relative weights appear to differ across musical styles.

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.

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.

The aim of this study is to manipulate musical cues systematically to determine the aspects of music that contribute to emotional expression, and whether these cues operate in additive or interactive fashion, and whether the cue levels can be characterized as linear or non-linear. An optimized factorial design was used with six primary musical cues (mode, tempo, dynamics, articulation, timbre, and register) across four different music examples. Listeners rated 200 musical examples according to four perceived emotional characters (happy, sad, peaceful, and scary). The results exhibited robust effects for all cues and the ranked importance of these was established by multiple regression. The most important cue was mode followed by tempo, register, dynamics, articulation, and timbre, although the ranking varied across the emotions. The second main result suggested that most cue levels contributed to the emotions in a linear fashion, explaining 77–89% of variance in ratings. Quadratic encoding of cues did lead to minor but significant increases of the models (0–8%). Finally, the interactions between the cues were non-existent suggesting that the cues operate mostly in an additive fashion, corroborating recent findings on emotional expression in music (Juslin and Lindström, 2010). peerReviewed

Social media services such as Last.fm provide crowd-sourced mood tags which are a rich but often noisy source of information. In contrast, editorial annotations from production music libraries are meant to be incisive in nature. We compare the efficiency of these two data sources in capturing semantic information on mood expressed by music. First, a semantic computing technique devised for mood-related tags in large datasets is applied to Last.fm and I Like Music (ILM) corpora separately (250,000 tracks each). The resulting semantic estimates are then correlated with listener ratings of arousal, valence and tension. High correlations (Spearman's rho) are found between the track positions in the dimensional mood spaces and listener ratings using both data sources (0.60

Machine learning (ML) techniques are becoming commonplace in business and research alike. With the automatization of data collection e fforts, evermore data is being captured, rendering the task of extracting insightful patterns increasingly challenging. In addition to this 'data avalanche' becoming evermore overwhelming, the usage of more computationally intensive algorithms in predictive analysis tasks also gives rise to new issues and challenges, so that a ML approach typically entails a trade off between computational effi ciency and predictive performance. In recent years, however, new paradigms in analytics have been proposed geared towards solving these data and computational challenges, including cloud computing, distributed computing, and parallel computing approaches. We set out to discern one of these new hypes in analytics, cloud computing, and present a case study hereof which was performed at KU Leuven. In this study, we set up a benchmarking experiment using the Microsoft Windows Azure cloud platform with Techila Technologies middleware, and compare the results with those obtained in a non-parallelized setup. The results show that significant analysis speed-ups can be gained when performing computational tasks in the cloud.

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.

The perceptual attributes of timbre have inspired a considerable amount of multidisciplinary research, but because of the complexity of the phenomena, the approach has traditionally been confined to laboratory conditions, much to the detriment of its ecological validity. In this study, we present a purely bottom-up approach for mapping the concepts that emerge from sound qualities. A social media ( http://www.last.fm ) is used to obtain a wide sample of verbal descriptions of music (in the form of tags) that go beyond the commonly studied concept of genre, and from this the underlying semantic structure of this sample is extracted. The structure that is thereby obtained is then evaluated through a careful investigation of the acoustic features that characterize it. The results outline the degree to which such structures in music (connected to affects, instrumentation and performance characteristics) have particular timbral characteristics. Samples representing these semantic structures were then submitted to a similarity rating experiment to validate the findings. The outcome of this experiment strengthened the discovered links between the semantic structures and their perceived timbral qualities. The findings of both the computational and behavioural parts of the experiment imply that it is therefore possible to derive useful and meaningful structures from free verbal descriptions of music, that transcend musical genres, and that such descriptions can be linked to a set of acoustic features. This approach not only provides insights into the definition of timbre from an ecological perspective, but could also be implemented to develop applications in music information research that organize music collections according to both semantic and sound qualities.

In this paper, we present the results of a pre-study on music performance analysis of ensemble music. Our aim is to implement a music classification system for the description of live recordings, for instance to help musicologist and musicians to analyze improvised ensemble performances. The main problem we deal with is the extraction of a suitable set of audio features from the recorded instrument tracks. Our approach is to extract rhythm-related audio features and to apply them for regression-based modeling of eight more general musical attributes. The model based on Partial Least-Squares Regression without preceding Principal Component Analysis performed best for all of the eight attributes.

In pulping and papermaking, dirt particles significantly affect the quality of paper. Knowledge of the dirt type helps to track the sources of the impurities which would considerably improve the paper making process. Dirt particle classification designed for this purpose should be adaptable because the dirt types are specific to the different processes of paper mills. This paper introduces a general approach for the adaptable classification system. The attention is paid to feature extraction and evaluation, in order to determine a suboptimal set of features for a certain data. The performance of standard classifiers on the provided data is presented, considering how the dirt particles or different types are classified. The effect of dirt particle grouping according to the particle size on the results of classification and feature evaluation is discussed. It is shown that the representative features of dirt particles from different size groups are different, which has an effect on the classification.

The rapid expansion of social media in music has provided the field with impressive datasets that offer insights into the semantic structures underlying everyday uses and classification of music. We hypothesize that the organization of these structures are rather directly linked with the ”qualia” of the music as sound. To explore the ways in which these structures are connected with the qualities of sounds, a semantic space was extracted from a large collection of musical tags with latent semantic and cluster analysis. The perceptual and musical properties of 19 clusters were investigated by a similarity rating task that used spliced musical excerpts representing each cluster. The resulting perceptual space denoting the clusters correlated high with selected acoustical features extracted from the stimuli. The first dimension related to the high-frequency energy content, the second to the regularity of the spectrum, and the third to the fluctuations within the spectrum. These findings imply that meaningful organization of music may be derived from low-level descriptions of the excerpts. Novel links with the functions of music embedded into the tagging information included within the social media are proposed. peerReviewed

Background Depression is a highly prevalent mood disorder, that impairs a person's social skills and also their quality of life. Populations affected with depression also suffer from a higher mortality rate. Depression affects person's ability to recognize emotions. We designed a novel experiment to test the hypothesis that depressed patients show a judgment bias towards negative emotions. Methods To investigate how depressed patients differ in their perception of emotions conveyed by musical examples, both healthy (n = 30) and depressed (n = 79) participants were presented with a set of 30 musical excerpts, representing one of five basic target emotions, and asked to rate each excerpt using five Likert scales that represented the amount of each one of those same emotions perceived in the example. Results Depressed patients showed moderate but consistent negative self-report biases both in the overall use of the scales and their particular application to certain target emotions, when compared to healthy controls. Also, the severity of the clinical state (depression, anxiety and alexithymia) had an effect on the self-report biases for both positive and negative emotion ratings, particularly depression and alexithymia. Limitations Only musical stimuli were used, and they were all clear examples of one of the basic emotions of happiness, sadness, fear, anger and tenderness. No neutral or ambiguous excerpts were included. Conclusions Depressed patients' negative emotional bias was demonstrated using musical stimuli. This suggests that the evaluation of emotional qualities in music could become a means to discriminate between depressed and non-depressed subjects. The practical implications of the present study relate both to diagnostic uses of such perceptual evaluations, as well as a better understanding of the emotional regulation strategies of the patients.

Neuroimaging studies investigating the processing of emotions have traditionally considered variance between subjects as statistical noise. However, according to behavioural studies, individual differences in emotional processing appear to be an inherent part of the process itself. Temporary mood states as well as stable personality traits have been shown to influence the processing of emotions, causing trait- and mood-congruent biases. The primary aim of this study was to explore how listeners' personality and mood are reflected in their evaluations of discrete emotions represented by music. A related aim was to investigate the role of personality in music preferences. An experiment was carried out where 67 participants evaluated 50 music excerpts in terms of perceived emotions (anger, fear, happiness, sadness, and tenderness) and preference. Current mood was associated with mood-congruent biases in the evaluation of emotions represented by music, but extraversion moderated the degree of mood-congruence. Personality traits were strongly connected with preference ratings, and the correlations reflected the trait-congruent patterns obtained in prior studies investigating self-referential emotional processing. Implications for future behavioural and neuroimaging studies on music and emotions are raised.

At the level of the auditory cortex, musicians discriminate pitch changes more accurately than nonmusicians. However, it is not agreed upon how sound familiarity and musical expertise interact in the formation of pitch-change discrimination skills, that is, whether musicians possess musical pitch discrimination abilities that are generally more accurate than in nonmusicians or, alternatively, whether they may be distinguished from nonmusicians particularly with respect to the discrimination of nonprototypical sounds that do not play a reference role in Western tonal music. To resolve this, we used magnetoencephalography (MEG) to measure the change-related magnetic mismatch response (MMNm) in musicians and nonmusicians to two nonprototypical chords, a “dissonant” chord containing a highly unpleasant interval and a “mistuned” chord including a mistuned pitch, and a minor chord, all inserted in a context of major chords. Major and minor are the most frequently used chords in Western tonal music which both musicians and nonmusicians are most familiar with, whereas the other chords are more rarely encountered in tonal music. The MMNm was stronger in musicians than in nonmusicians in response to the dissonant and mistuned chords, whereas no group difference was found in the MMNm strength to minor chords. Correspondingly, the length of musical training correlated with the MMNm strength for the dissonant and mistuned chords only. Our findings provide evidence for superior automatic discrimination of nonprototypical chords in musicians. Most likely, this results from a highly sophisticated auditory system in musicians allowing a more efficient discrimination of chords deviating from the conventional categories of tonal music.

In this paper, we present an analysis of the associations between emotion categories and audio features automatically extracted from raw audio data. This work is based on 110 excerpts from film soundtracks evaluated by 116 listeners. This data is annotated with 5 basic emotions (fear, anger, happiness, sadness, tenderness) on a 7 points scale. Exploiting state-of-the-art Music Information Retrieval (MIR) techniques, we extract audio features of different kind: timbral, rhythmic and tonal. Among others we also compute estimations of dissonance, mode, onset rate and loudness. We study statistical relations between audio descriptors and emotion categories confirming results from psychological studies. We also use machine-learning techniques to model the emotion ratings. We create regression models based on the Support Vector Regression algorithm that can estimate the ratings with a correlation of 0.65 in average.

In the study of music emotions, Valence is usually referred to as one of the dimensions of the circumplex model of emotions that describes music appraisal of happiness, whose scale goes from sad to happy. Nevertheless, related literature shows that Valence is known as being particularly difficult to be predicted by a computational model. As Valence is a contextual music feature, it is assumed here that its prediction should also require contextual music descriptors in its predicting model. This work describes the usage of eight contextual (also known as higher-level) descriptors, previously developed by us, to calculate happiness in music. Each of these descriptors was independently tested using the correlation coefficient of its prediction with the mean rating of Valence, reckoned by thirty-five listeners, over a piece of music. Following, a linear model using this eight descriptors was created and the result of its prediction, for the same piece of music, is described and compared with two other computational models from the literature, designed for the dynamic prediction of music emotion. Finally it is proposed here an initial investigation on the effects of expressive performance and musical structure on the prediction of Valence. Our descriptors are then separated in two groups: performance and structural, where, with each group, we built a linear model. The prediction of Valence given by these two models, over two other pieces of music, are here compared with the correspondent listeners' mean rating of Valence, and the achieved results are depicted, described and discussed.

The literature of visual quality is mainly concentrated on devising new physical, visual, or computational quality features which could indirectly reflect ldquotrue visual qualityrdquo. The problem is that the true visual quality is always a subjective and context sensitive judgement of a single individual or a group of individuals. Therefore, the developed methods are only loosely connected to this ultimate objective, and the existing de facto and official standards have been designed by forming a consensus among experts of a specific field (e.g., in the printing industry). In this study, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Then we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation trial. Finally by using the above framework, we devise the best combinations from a set of well-known measurable quantities. The devised combinations can be considered as optimal when agreement with the human visual quality experience is desired, and therefore, they also reveal completely novel information about measuring visual quality.

One of the most important research goals in media science is a computational model for the human perception of visual quality, that is, how to predict the subjective visual quality experience. This research area has converged to developing new and investigating existing lower-level measurable quantities, physical, visual or computational, which could explain the high level experience. A principal research question, whether the prediction of the visual quality experience based on any lower-level objective measurements is possible at all, has received much less attention. This question is investigated in this study. First, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Second, we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation. Finally, the presented ranking method is used to provide quantitative evidence that visual quality experience can be predicted using lower-level measurable quantities. This result is novel and by simultaneously revealing the underlying lower-level factors it should re-direct the future research towards the true model.

Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for specifying the quality potential of printers and materials in more detail than before. Both production and end-use standpoints are relevant. This paper gives an overview of an on-going study which has the goal of determining a framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is founded on the concept of a layered network model. The model and its subjective, objective and instrumental measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image content has a marked effect on the dimensions. This underlines the challenges in designing the test images.

Computational analysis of large musical corpora provides an approach that overcomes some of the limitations of manual analysis related to small sample sizes and subjectivity. The present paper aims to provide an overview of the computational approach to music research. It discusses the issues of music representation, musical feature extraction, digital music collections, and data mining techniques. Moreover, it provides examples of visualization of large musical collections.

The ultimate print quality evaluation is always based on endusers' "quality experience", and therefore, the main challenge in automatic evaluation is to model the visual path and cognition process from physical properties to the experience. The present efforts to automate print quality evaluation have been concentrated on automating the current manually-performed assesments, which reduces the laborious work, but does not provide any novel information. In this work, a new approach for automating the evaluation is proposed and the approach is utilised by defining new computational level features which are able to explain visual quality evaluations performed by human experts.

The performance of autocorrelation-based meter induction was tested with two large collections of folk melodies, consisting of approximately 13 000 melodies for which the correct meters were available. The performance was measured by the proportion of melodies whose meter was correctly classified by a discriminant function. Furthermore, it was examined whether including different melodic accent types would improve the classification performance. By determining the components of the autocorrelation functions that were significant in the classification it was found that periodicity in note onset locations was the most important cue for the determination of meter. Of the melodic accents included, Thomassen's melodic accent was found to provide the most reliable cues for the determination of meter. The discriminant function analyses suggested that periodicities longer than one measure may provide cues for meter determination that are more reliable than shorter periodicities. Overall, the method predicted notated meter with an accuracy reaching 96% for binary classification and 75% for classification into nine categories of meter.

Measuring the visual quality of printed media is important since printed products have an important role in everyday life. Finding ways to automatically predict the image quality has been an active research topic in digital image processing, but adapting those methods to measure the visual quality of printed media has not been studied often or in depth and is not straightforward. Here, we analyze the efficacy of no-reference image quality assessment (IQA) algorithms originally developed for digital IQA with regards to predicting the perceived quality of printed natural images. We perform a comprehensive statistical comparison of the methods. The best methods are shown to accurately predict subjective opinions of the quality of printed photographs using data from a psychometric study. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10 .1117/1.JEI.23.6.061106)

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.

Full reference image quality algorithms are standard tools in digital image processing but have not been utilized for printed images due to a "correspondence gap" between the digital domain (a reference) and physical domain (printed sample). In this work, the authors propose a framework for applying full reference image quality algorithms to printed images. The framework consists of accurate scanning of printed samples and automatic registration and descreening procedures which bring the scans in correspondence with their digital originals. The authors complete the framework by incorporating state-of-the-art full reference algorithms to it. Using data from comprehensive psychometrical experiments of subjective quality experience, the authors benchmark the state-of-the-art methods and point out similar results in the digital domain: the best digital full reference measures, such as the recently introduced visual information fidelity algorithm, perform best also for printed media.

Interpersonal musical entrainment—temporal synchronization and coordination between individuals in musical contexts—is a ubiquitous phenomenon related to music’s social functions of promoting group bonding and cohesion. Mechanisms other than sensorimotor synchronization are rarely discussed, while little is known about cultural variability or about how and why entrainment has social effects. In order to close these gaps, we propose a new model that distinguishes between different components of interpersonal entrainment: sensorimotor synchronization—a largely automatic process manifested especially with rhythms based on periodicities in the 100–2000 ms timescale—and coordination, extending over longer timescales and more accessible to conscious control. We review the state of the art in measuring these processes, mostly from the perspective of action production, and in so doing present the first cross-cultural comparisons between interpersonal entrainment in natural musical performances, with an exploratory analysis that identifies factors that may influence interpersonal synchronization in music. Building on this analysis we advance hypotheses regarding the relationship of these features to neurophysiological, social, and cultural processes. We propose a model encompassing both synchronization and coordination processes and the relationship between them, the role of culturally shared knowledge, and of connections between entrainment and social processes.

We present MIRToolbox, an integrated set of functions written in Matlab, dedicated to the extraction from audio files of musical features related, among others, to timbre, tonality, rhythm or form. The objective is to offer a state of the art of computational approaches in the area of Music Information Retrieval (MIR). The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches — including new strategies we have developed —, that users can select and parametrize. These functions can adapt to a large area of objects as input.

A method for the detection of bubble-like transparent objects with multiple interfaces in a liquid is proposed. Depending on the lighting conditions, bubble appearance varies significantly, including contrast reversal and multiple inter-reflections. We formulate the bubble detection problem as the detection of Concentric Circular Arrangements (CCA). The CCAs are recovered in a hypothesize-optimize-verify framework. The hypothesis generation proceeds by sampling from the components of the non-maximum suppressed responses of oriented ridge filters followed by CCA parameter estimation. Parameter optimization is carried out by minimizing a novel cost-function by the simplex method. The proposed method for bubble detection showed good performance in an industrial application requiring estimation of gas volume in pulp suspension, achieving 1.5% mean absolute relative error.

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Various models of melodic similarity have been proposed and assessed in perceptual experiments. Contour and pitch content variables haven been favoured although music-theoretical and statistical variables have also been claimed to explain similarity ratings. A Re-analysis of earlier work by Rosner & Meyer (1986) suggests that simple contextual features can also be highly explanatory with more complex stimuli. A new experiment containing short melodic phrases investigated the effectiveness of several global and comparative variables. A multi-dimensional scaling solution indicated that both melodic direction and pitch range are highly relevant for making such similarity judgments and that the most salient aspects of melody when making similarity judgments are relatively simple context-dependent features.

Listeners are sensitive to pitch distributional information in music (N. Oram & L. L. Cuddy, 1995; C. L. Krumhansl, J. Louhivuori, P.Toiviainen, T. Järvinen, & 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, että musiikin tilastollisilla tapahtumilla, kuten sävelten määrillä ja tyypillisillä intervalleilla, on merkitystä, kun kuulijat muodostavat käsityksiään musiikin rakenteesta (N. Oram & L. L. Cuddy, 1995; C. L. Krumhansl, J. Louhivuori, P. Toiviainen, T. Järvinen, & T. Eerola, 1999). Näiden piirteiden voidaan olettaa olevan tärkeitä myös musiikin luokittelussa. Toistaiseksi ei kuitenkaan tiedetä, miten hyvin tilastollisilla piirteillä voitaisiin musiikin luokittelua selittää. Tätä testattiin kuulijoille järjestetyn samanlaisuusarviointitehtävän avulla. Tutkimuksen materiaali koostui 15 melodiasta, jotka edustivat viittä eri kansanmusiikkityyliä. Regressioanalyysi paljasti, että musiikin tilastollisten piirteiden samanlaisuus pystyi selittämään kohtuullisen määrän (40%) kuulijoiden antamista samanlaisuusarvioista. Hieman parempi selitysaste (55%) saavutettiin kuvaavilla muuttujilla, joita olivat melodian laajuus ja ennakoitavuus sekä rytmin vaihtelevuus. Näin ollen tulokset antavat aiheen olettaa, että musiikin tilastolliset piirteet ja kuvailevat muuttujat vaikuttavat kuulijoiden luokittelupäätöksiin.

This poster deals with an action-research project currently being undertaken at the Faculty of Education of the University of Bologna about the observation of musical making during the daily routines with children under-four. The questions are : What are the musical dimensions of the daily routines of young children ? How do these dimensions interact with children's musical development ? What are the constants and the variants between the routines in family and at the nursery? What is the role of the adult ? This poster present the observations made during the diaper change, at home and at the nursery.