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The aim of the present study is to determine which acoustic components of harmonic consonance and dissonance influence automatic responses in a simple cognitive task. In a series of affective priming experiments, eight pairs of musical intervals were used to measure the influence of acoustic roughness and harmonicity on response times in a word-classification task conducted online. Interval pairs that contrasted in roughness induced a greater degree of affective priming than pairs that did not contrast in terms of their roughness. Contrasts in harmonicity did not induce affective priming. A follow-up experiment used detuned intervals to create higher levels of roughness contrasts. However, the detuning did not lead to any further increase in the size of the priming effect. More detailed analysis suggests that the presence of priming in intervals is binary: in the negative primes that create congruency effects the intervals' fundamentals and overtones coincide within the same equivalent rectangular bandwidth (i.e., the minor and major seconds). Intervals that fall outside this equivalent rectangular bandwidth do not elicit priming effects, regardless of their dissonance or negative affect. The results are discussed in the context of recent developments in consonance/dissonance research and vocal similarity.

Several computer systems have been designed for music emotion research that aim to identify how different structural or expressive cues of music influence the emotions conveyed by the music. However, most systems either operate offline by pre-rendering different variations of the music or operate in real-time but focus mostly on structural cues. We present a new interactive system calledEmoteControl, which allows users to make changes to both structural and expressive cues (tempo, pitch, dynamics, articulation, brightness, and mode) of music in real-time. The purpose is to allow scholars to probe a variety of cues of emotional expression from non-expert participants who are unable to articulate or perform their expression of music in other ways. The benefits of the interactive system are particularly important in this topic as it offers a massive parameter space of emotion cues and levels for each emotion which is challenging to exhaustively explore without a dynamic system. A brief overview of previous work is given, followed by a detailed explanation ofEmoteControl’s interface design and structure. A portable version of the system is also described, and specifications for the music inputted in the system are outlined. Several use-cases of the interface are discussed, and a formal interface evaluation study is reported. Results suggested that the elements controlling the cues were easy to use and understood by the users. The majority of users were satisfied with the way the system allowed them to express different emotions in music and found it a useful tool for research.

Two complementary aspects of interpersonal entrainment – synchronization and movement coordination – are explored in North Indian classical instrumental music, in the auditory and visual domains respectively. Sensorimotor synchronization (SMS) is explored by analysing pairwise asynchronies between the event onsets of instrumental soloists and their tabla accompanists, and the variability of asynchrony by factors including tempo, dynamic level and metrical position is explored. Movement coordination is quantified using cross wavelet transform (CWT) analysis of upper body movement data, and differences in CWT Energy are investigated in relation to the metrical and cadential structures of the music. The analysis demonstrates that SMS within this corpus varies significantly with tempo, event density, peak levels and leadership. Effects of metrical position on pairwise asynchrony are small and offer little support for the hypothesis of lower variability in synchronization on strong metrical positions; a larger difference was found at cadential downbeats, which show increased melody lead. Movement coordination is greater at metrical boundaries than elsewhere, and most strikingly is greater at cadential than at other metrical downbeats. The implications of these findings for understanding performer coordination are discussed in relation to ethnographic research on the genre.

Monitoring plankton is important as they are an essential part of the aquatic food web as well as producers of oxygen. Modern imaging devices produce a massive amount of plankton image data which calls for automatic solutions. These images are characterized by a very large variation in both the size and the aspect ratio. Convolutional neural network (CNN) based classification methods, on the other hand, typically require a fixed size input. Simple scaling of the images into a common size contains several drawbacks. First, the information about the size of the plankton is lost. For human experts, the size information is one of the most important cues for identifying the species. Second, downscaling the images leads to the loss of fine details such as flagella essential for species recognition. Third, upscaling the images increases the size of the network. In this work, extensive experiments on various approaches to address the varying image dimensions are carried out on a challenging phytoplankton image dataset. A novel combination of methods is proposed, showing improvement over the baseline CNN. Post-print / Final draft

Online studies using recruitment services (such as Prolific or Amazon's MTurk) and online testing platforms (such as Gorilla or PsyToolkit) are becoming increasingly common in psychological science. Although auditory disciplines have been slower to adopt these methods, uptake is rapidly increasing in auditory perception and cognition research. Utilising online data collection and recruitment presents several advantages to researchers in terms of the speed of research and the range of target demographics available compared to either traditional lab studies or web-based recruitment via traditional means. Online platforms and recruitment services also present a set of technical and ethical challenges owing to the fact that the people completing experiments are working with their own devices from their homes. This article discusses the potential technical and ethical implications of online studies, including both recruitment services and online testing platforms, with specific reference to auditory perception and cognition research. Rates of remuneration, sampling characteristics, anonymity, quality control, and ethics are all discussed with respect to these approaches. We also provide proposals for how researchers can ensure that online research meets present-day ethical and technical guidelines as well as research transparency standards

Many people enjoy sad music, and the appeal for tragedy is widespread among the consumers of film and literature. The underlying mechanisms of such aesthetic experiences are not well understood. We tested whether pleasure induced by sad, unfamiliar instrumental music is explained with a homeostatic or a reward theory, each of which is associated with opposite patterns of changes in the key hormones. Sixty-two women listened to sad music (or nothing) while serum was collected for subsequent measurement of prolactin (PRL) and oxytocin (OT) and stress marker (cortisol and adrenocorticotropic hormone) concentrations. Two groups of participants were recruited on the basis of low and high trait empathy. In the high empathy group, PRL and OT levels were significantly lower with music compared with no music. And compared to the low empathy group, the high empathy individuals reported an increase of positive mood and higher ratings of being moved with music. None of the stress markers showed any changes across the conditions or the groups. These hormonal changes, inconsistent with the homeostatic theory proposed by Huron, exhibit a pattern expected of general reward. Our findings illuminate how unfamiliar and low arousal music may give rise to pleasurable experiences.

International audience; Music is often intimately linked to identity, as evidenced by the high value many people place on musical activities and the way in which music can become seemingly effortlessly coupled to important memories from throughout one’s lifespan. Previous research has revealed a consistent reminiscence bump in autobiographical memory—the disproportionate recall of memories from between ages 10 to 30 years in comparison with other lifetime periods—which also appears to extend to music-related memories. The present study represents one of the largest explorations of the musical reminiscence bump across adulthood to date. Participants (N = 470; ages 18 to 82 years) were shown the titles and artists of 111 popular songs that had featured in the charts between 1950 and 2015 and rated the degree to which they had autobiographical memories associated with each song, as well as the degree to which they were familiar with and liked the song. We found a reminiscence bump in adolescence (peaking around age 14) for both ratings of the autobiographical salience of songs featured in the charts during that period and the familiarity of these songs. Liking ratings showed more divergent results depending on a participant’s current age, including evidence for a cascading reminiscence bump, in which liking ratings from young adults increased for music from their parents’ adolescent years. We also revealed new evidence that music-related autobiographical memories appear to invoke similar retrieval processes to the common methodology of eliciting autobiographical memories via word cues. We contextualize these results in relation to general theoretical accounts of the reminiscence bump, and age-related differences in the bump are discussed in relation to various sociocultural and technological changes in music listening habits

The aim of the present study is to determine which acoustic components of harmonic consonance and dissonance influence automatic responses in a simple cognitive task. In a series of experiments, ten musical interval pairs were used to measure the influence of acoustic roughness and harmonicity on response times in an affective priming task conducted online. There was a significant correlation between the difference of roughness for each pair of intervals and a response time index. Harmonicity did not influence response times on the cognitive task. More detailed analysis suggests that the presence of priming in intervals is binary: in the negative primes that create congruency effects the intervals’ fundamentals and overtones coincide within the same equivalent rectangular bandwidth (i.e. the minor and major seconds). Intervals that fall outside this equivalent rectangular bandwidth do not elicit priming effects, regardless of their dissonance or cultural conventions of negative affect. The results are discussed in the context of recent developments in consonance/dissonance research and vocal similarity.

Plankton communities form the basis of aquatic ecosystems and elucidating their role in increasingly important environmental issues is a constantly present research question. The concealed plankton community dynamics reflect changes in environmental forcing, growth traits of competing species, and multiple food web interactions. Recent technological advances have led to the possibility of collecting real-time big data opening new horizons for testing core hypotheses in planktonic systems, derived from macroscopic realms, in community ecology, biodiversity research, and ecosystem functioning. Analyzing the big data calls for computer vision and machine learning methods capable of producing interoperable data across platforms and systems. In this paper we apply convolutional neural networks (CNN) to classify a brackish-water phytoplankton community in the Baltic Sea. For solving the classification task, we utilize compact CNN architectures requiring less computational capacity and creating an opportunity to quickly train the network. This makes it possible to (1) test various modifications to the classification method, and (2) repeat each experiment multiple times with different training and test set combinations to obtain reliable results. We further analyze the effect of large class imbalance to the CNN performance, and test relevant data augmentation techniques to improve the performance. Finally, we address the practical implications of the classification performance to aquatic research by analyzing the confused classes and their effect on the reliability of the automatic plankton recognition system, to guide further development of plankton recognition research. Our results show that it is possible to obtain good classification accuracy with relatively shallow architectures and a small amount of training data when using effective data augmentation methods even with a very unbalanced dataset.

After losing a close other, individuals usually confide in an empathic friend to receive comfort and they seem to have a heightened desire for mood-congruent, consoling music. Hence, it has been proposed that affect-congruent music acts as a social surrogate for an empathic friend. Thus, we hypothesized that listening to comforting music, as a response to a social loss experience, provides a sense of empathic company as indicated by reduced loneliness and heightened empathy. We further predicted that distracting music would have a stronger impact on the listeners’ mood in comparison to comforting pieces. To test these assumptions, an experiment with two factors was designed: (1) Sadness was induced by an approved guided imagery method where participants visualized either their father dying (social loss), losing their eyesight (non-social loss), or shopping for groceries (control condition). (2) After the mood induction, the listening task included either comforting or distracting music that participants selected themselves. Psychometric measures for mood and loneliness were collected before and after the mood induction and after the music listening. The data were analyzed with mixed model ANOVAs. The results showed a significant reduction of loneliness and a relevant rise in empathy and mood due to listening to self-selected music, irrespective of the listener’s mood or the applied listening strategy, which suggests that private musical engagement in general can provide mood-repair and a sense of connection. This beneficial effect of private musical engagement supports the notion that not only music production but also its perception engenders social cognition. Overall, the findings corroborate the idea of music as a social surrogate.

In this paper we propose a method to match pelage patterns of the Saimaa ringed seals enabling the re-identification of individuals. First, the pelage pattern is extracted from the seal's fur using a method based on the Sato tubeness filter. After this, the similarities of the pelage pattern patches are computed using a siamese network trained with a triplet loss function and a large dataset of manually selected patches. The similarities are then used to find the best matching patches from the images in the database of known individuals. Furthermore, we employ the proposed pattern matching method to build a full framework for the ringed seal re-identification, consisting of CNN-based animal segmentation, patch correspondence detection, and ranking the images in the database of known seal individuals based on the similarity to the query image. Our experiments on challenging datasets of Saimaa ringed seals show that the proposed method achieves promising identification results, providing a useful tool for the Saimaa ringed seal monitoring. Post-print / Final draft

In order to develop better touch and gesture user interfaces, it is important to be able to measure how humans move their hands while interacting with technical devices. The recent advances in high-speed imaging technology and in image-based object tracking techniques have made it possible to accurately measure the hand movement from videos without the need for data gloves or other sensors that would limit the natural hand movements. In this paper, we propose a complete framework to measure hand movements in 3D in human-computer interaction situations. The framework includes the composition of the measurement setup, selecting the object tracking methods, post-processing of the motion trajectories, 3D trajectory reconstruction, and characterizing and visualizing the movement data. We demonstrate the framework in a context where 3D touch screen usability is studied with 3D stimuli. Post-print / Final draft

Reaction time (RT) methods have been a mainstay of research in cognitive psychology for over a century. RT methods have been applied in domains as diverse as visual perception (e.g., Ando et al., 2002), personality traits (e.g., Robinson and Tamir, 2005), and social psychology (e.g., Wang et al., 2017). In music cognition, RT methods have been used as an indirect measure of several phenomena such as harmonic expectation (Bharucha and Stoeckig, 1986), melodic expectation (Aarden, 2003) cross modal priming (Goerlich et al., 2012), absolute pitch (Miyazaki, 1989; Bermudez and Zatorre, 2009), and emotional responses (Bishop et al., 2009). Traditionally, reaction time data has been collected in a lab. However, recent years have seen the development of software capable of collecting accurate response time data online, for instance PsyToolkit (Stoet, 2010, 2017), PsychoPy (Peirce et al., 2019), Gorilla (Anwyl-Irvine et al., 2019), and Qualtrics' QRTEngine (Barnhoorn et al., 2015) amongst others. In the early days of web-based reaction time studies, there was considerable skepticism about the viability of RT data collected online. Despite the prevalence of software specifically designed to collect reaction time data online, and the increasing incidence of Web-based data collection, there remains a degree of caution around online reaction time studies. However, recent research (Barnhoorn et al., 2015; de Leeuw and Motz, 2016; Hilbig, 2016) suggests that online reaction time data is perhaps more trustworthy than was previously thought, but these studies have not yet involved music as stimuli. Alongside the developments in software, recruitment of participants in online studies has been made easier by the prevalence of social media and crowdsourcing platforms such as Amazon's MTurk service and Prolific. Not surprisingly, the use of crowdsourced samples by researchers is growing rapidly (Stewart et al., 2017). However, to the authors' knowledge (with the exception of de Leeuw and Motz, 2016) the comparisons of laboratory and online RT data have focused on descriptive measures of the RT distributions, and relatively little attention has been paid to the agreement between the RT distributions as a whole. Moreover, none of these studies considers phenomena associated with music cognition. Given the widespread use of RT methods in music cognition and the growth of crowdsourcing as a recruitment tool, the authors consider there to be a need to test the viability of online RT collection specifically in the case of music cognition. The present data report offers the results of a response time task completed in three different contexts—in a standard lab setting (“Lab”), online recruited via “traditional” online techniques (“Web”) and crowdsourced vis Prolific.ac (“CS”). Below, we present summary data for the three data sets before testing the comparability of the three data sets on an item-by-item basis.

Previous literature suggests that structural and expressive cues affect the emotion expressed in music. However, only a few systematic explorations of cues have been done, usually focussing on a few cues or a limited amount of predetermined arbitrary cue values. This paper presents three experiments investigating the effect of six cues and their combinations on the music's perceived emotional expression. Twenty-eight musical pieces were created with the aim of providing flexible, ecologically valid, unfamiliar, new stimuli. In Experiment 1, 96 participants assessed which emotions were expressed in the pieces using Likert scale ratings. In Experiment 2, a subset of the stimuli was modified by participants (N = 42) via six available cues (tempo, mode, articulation, pitch, dynamics, and brightness) to convey seven emotions (anger, sadness, fear, joy, surprise, calmness, and power), addressing the main aim of exploring the impact of cue levels to expressions. Experiment 3 investigated how well the variations of the original stimuli created by participants in Experiment 2 expressed their intended emotion. Participants (N = 91) rated them alongside the seven original pieces, allowing the exploration of similarities and differences between the two sets of related pieces. An overall pattern of cue combinations was identified for each emotion. Some findings corroborate previous studies: mode and tempo were the most impactful cues in shaping emotions, and sadness and joy were amongst the most accurately recognised emotions. Novel findings include soft dynamics being used to convey anger, and dynamics and brightness being the least informative cues. These findings provide further motivation to investigate the effect of cues on emotions in music as combinations of multiple cues rather than as individual cues, as one cue might not give enough information to portray a specific emotion. The new findings and discrepancies are discussed in relation to current theories of music and emotions.

The social surrogacy hypothesis holds that people resort to temporary substitutes, so-called social surrogates, if direct social interaction is not possible. In this exploratory study, we investigate social motives for listening to music in comparison to watching TV and reading fiction. Thirty statements about possible social reasons for the engagement with media were compiled. After 374 participants had rated their agreement with those statements, they were reduced to seven categories: Company, Shared experiences, Understanding others, Reminiscence, Isolation, Group identity, and Culture. The results propose that music is used as temporary substitute for social interaction alongside TV programs and fiction, but that it acts differently. Music listening might act as a social surrogate by evoking memories of relationship partners or through identification processes. There are overlapping motives between the domains, but the elicitation of nostalgia appears to be unique to music listening. The results motivate further investigation into the effects of music listening on socio-emotional well-being.

The recent surge of interest towards the paradoxical pleasure produced by sad music has generated a handful of theories and an array of empirical explorations on the topic. However, none of these have attempted to weigh the existing evidence in a systematic fashion. The present work puts forward an integrative framework laid out over three levels of explanation – biological, psycho-social, and cultural – to compare and integrate the existing findings in a meaningful way. First, we review the evidence pertinent to experiences of pleasure associated with sad music from the fields of neuroscience, psychophysiology, and endocrinology. Then, the psychological and interpersonal mechanisms underlying the recognition and induction of sadness in the context of music are combined with putative explanations ranging from social surrogacy and nostalgia to feelings of being moved. Finally, we address the cultural aspects of the paradox – the extent to which it is embedded in the Western notion of music as an aesthetic, contemplative object – by synthesising findings from history, ethnography, and empirical studies. Furthermore, we complement these explanations by considering the particularly significant meanings that sadness portrayed in art can evoke in some perceivers. Our central claim is that one cannot attribute the enjoyment of sadness fully to any one of these levels, but to a chain of functionalities afforded by each level. Each explanatory level has several putative explanations and its own shift towards positive valence, but none of them deliver the full transformation from a highly negative experience to a fully enjoyable experience alone. The current evidence within this framework ranges from weak to non-existent at the biological level, moderate at the psychological level, and suggestive at the cultural level. We propose a series of focussed topics for future investigation that would allow to deconstruct the drivers and constraints of the processes leading to pleasurable music-related sadness. peerReviewed

Detection, counting and characterization of bubbles, that is, transparent objects in a liquid, is important in many industrial applications. These applications include monitoring of pulp delignification and multiphase dispersion processes common in the chemical, pharmaceutical, and food industries. Typically the aim is to measure the bubble size distribution. In this paper, we present a comprehensive comparison of bubble detection methods for challenging industrial image data. Moreover, we compare the detection-based methods to a direct bubble size distribution estimation method that does not require the detection of individual bubbles. The experiments showed that the approach based on a convolutional neural network (CNN) outperforms the other methods in detection accuracy. However, the boosting-based approaches were remarkably faster to compute. The power spectrum approach for direct bubble size distribution estimation produced accurate distributions and it is fast to compute, but it does not provide the spatial locations of the bubbles. Selecting the most suitable method depends on the specific application.

The proposed image analysis method allows the measurement of organic phase droplet sizes, velocities, and copper concentrations in single droplet column copper extraction using hydroxyoxime complexation. The method uses image acquisition sequences from video, detection of moving droplets, binarization of background subtracted images, and noise reduction from images. The image analysis method enabled characterizing the shape of droplets, by determining the droplet minor and major axis lengths. The method can detect droplet concentration directly inside the column wherever the droplet is visible. Image based method was validated against reference samples which were analyzed using spectrophotometry. The traditional concentration measurement using the spectrophotometric analysis of column outlet sample collection was performed for comparison purposes. The direct image analysis showed smaller variation in mass transfer results because of longer and non-uniform residence times when using sample collection. However, separately collected sample analysis together with the image analysis enables determination of the copper mass transfer during all the three steps of column experiment. Image analysis can also be used to reveal concentration profiles inside the droplet. This method is not limited to extractants, but it can be applied to systems where a suitable color change is present depending on camera sensor technology.

Mixing performance in continuous rotary drums has been studied. The video analysis method was developed to evaluate different configurations of straight lifters in the rotary drum. The method converts a captured video into a single image, called stack image. The color marker tracking was estimated based on the color saturation of the stack image. Coefficients of variation and mixing indices were calculated from the color saturation profiles for different straight blade lifter configurations. The video analysis method was confronted to the impulse response of acid concentrations in water solutions. The developed analysis method has been superior with viscous fluids compared to traditional tracer impulse method in mixing evaluations. Water and 1% CMC-water solution were used in this mixing study for covering broadly different viscous materials. The drum lengthwise results for one lifter configuration were obtained from a single experiment due to the block representation of the image analysis method. It enab...

Numerous computer systems have been designed for music emotion research, aiming to identify how different structural and expressive cues of a musical piece affect the emotion conveyed by the music and perceived by the listener. However, most systems are either offline systems, which work by pre-rendering different variations of the music, or real-time systems, which focus mostly on expressive cues. This paper presents a new system called EmoteControl, which allows changes to be made to both structural and expressive cues (tempo, pitch, dynamics, articulation, brightness, and mode) of a musical piece while it plays in realtime. First, a brief overview of previous computer systems will be given, followed by a detailed explanation of EmoteControl's interface design and structure. A smaller-scale version of the system will also be described, and specifications for the music inputted in the system will be outlined. Interface limitations will be noted, and user evaluation cases for both versions of the interface will be discussed. A demonstration of the interface will be presented, featuring specifically composed musical pieces.

Art brings rich, pleasurable experiences to our daily lives. However, many theories of art and aesthetics focus on specific strong experiences—in the contexts of museums, galleries, and concert halls and the aesthetic perception of canonized arts—disregarding the impact of daily experiences. Furthermore, pleasure is often treated as a simplistic concept of merely positive affective character, yet recent psychological research has revealed the experience of pleasure is far more complicated. This study explored the nature of pleasure evoked by everyday aesthetic objects. A mixture of statistical and qualitative methods was applied in the analysis of the data collected through a semi-structured online survey ( N = 464). The result asserts the experience of emotional ambivalence occurred and was composed of a variety of nuanced emotions and related association, rather than just a combination of contradicting emotions. Such paradoxical pleasure is defined as a self-conscious hedonic exposure to negative emotions in art reception. The study also depicted four types of attitudinal ambivalence: loss, diversity, socio-ideology, and distance, reflecting contextual elements intertwined into experience, and the connection between ambivalence and intense emotional experience.

International audience; Experiencing pleasure and displeasure is a fundamental part of life. Hedonics guide behavior, affect decision-making, induce learning, and much more. As the positive and negative valence of feelings, hedonics are core processes that accompany emotion, motivation, and bodily states. Here, the affective neuroscience of pleasure and displeasure that has largely focused on the investigation of reward and pain processing, is reviewed. We describe the neurobiological systems of hedonics and factors that modulate hedonic experiences (e.g., cognition, learning, sensory input). Further, we review maladaptive and adaptive pleasure and displeasure functions in mental disorders and well-being, as well as the experience of aesthetics. As a centerpiece of the Human Affectome Project, language used to express pleasure and displeasure was also analyzed, and showed that most of these analyzed words overlap with expressions of emotions, actions, and bodily states. Our review shows that hedonics are typically investigated as processes that accompany other functions, but the mechanisms of hedonics (as core processes) have not been fully elucidated.

Studies have suggested that visual imagery forms an important part of the listening experience and might be one of the mechanisms by which music induces emotions in a listener. However, little is known about the content, prevalence, and function of visual imagery during music listening. To that end, an online survey was constructed to explore music-related visual imagery. This included 24 statements about visual imagery based on previous research and an open question regarding the content of their inner images. Several standardized questionnaires (Vividness of Visual Imagery Questionnaire and Goldsmiths Musical Sophistication Index) were included as well to investigate the link to visual imagery in non-musical contexts and across individuals with various levels of musical training. In total, 669 participants provided responses to an online survey. A factorial structure of music and visual imagery statements provided a three-factor structure consisting of vivid, soothing, and disruptive visual imagery, although the actual factor structure was nonidentical between the musically trained and untrained respondents. Separate analyses of factors for musically trained and untrained participants yielded a more parsimonious structure of visual imagery, which consisted of vivid and soothing visual imagery. These two factors consistently exhibited different weights across the items; for musically trained participants, vivid imagery was more related to modulating arousal than for untrained participants. The ability to conjure up vivid visual imagery was only weakly related to the presence of music-related visual imagery. A content analysis of the open question revealed common themes that related to a mixture of concrete visual imagery (landscapes, images of people, and scenes from past events) and abstract visual imagery (shapes, objects, and colors). Implications of these findings for further studies on music-induced emotions are discussed with a focus on a recent constructionist account of emotional meanings in music.

Pectoral muscle segmentation is a crucial step in various computer-aided applications of breast Magnetic Resonance Imaging (MRI). Due to imaging artifact and homogeneity between the pectoral and breast regions, the pectoral muscle boundary estimation is not a trivial task. In this paper, a fully automatic segmentation method based on deep learning is proposed for accurate delineation of the pectoral muscle boundary in axial breast MR images. The proposed method involves two main steps: pectoral muscle segmentation and boundary estimation. For pectoral muscle segmentation, a model based on the U-Net architecture is used to segment the pectoral muscle from the input image. Next, the pectoral muscle boundary is estimated through candidate points detection and contour segmentation. The proposed method was evaluated quantitatively with two real-world datasets, our own private dataset, and a publicly available dataset. The first dataset includes 12 patients breast MR images and the second dataset consists of 80 patients breast MR images. The proposed method achieved a Dice score of 95% in the first dataset and 89% in the second dataset. The high segmentation performance of the proposed method when evaluated on large scale quantitative breast MR images confirms its potential applicability in future breast cancer clinical applications. Post-print / Final draft

Transmission electron microscopy (TEM) provides information about Inorganic nanoparticles that no other method is able to deliver. Yet, a major task when studying Inorganic nanoparticles using TEM is the automated analysis of the images, i.e. segmentation of individual nanoparticles. The current state-of-the-art methods generally rely on binarization routines that require parameterization, and on methods to segment the overlapping nanoparticles (NPs) using highly idealized nanoparticle shape models. It is unclear, however, that there is any way to determine the best set of parameters providing an optimal segmentation, given the great diversity of NPs characteristics, such as shape and size, that may be encountered. Towards remedying these barriers, this paper introduces a method for segmentation of NPs in Bright Field (BF) TEM images. The proposed method involves three main steps: binarization, contour evidence extraction, and contour estimation. For the binarization, a model based on the U-Net architecture is trained to convert an input image into its binarized version. The contour evidence extraction starts by recovering contour segments from a binarized image using concave contour points detection. The contour segments which belong to the same nanoparticle are grouped in the segment grouping step. The grouping is formulated as a combinatorial optimization problem and solved using the well-known branch and bound algorithm. Finally, the full contours of the NPs are estimated by an ellipse. The experiments on a real-world dataset consisting of 150 BF TEM images containing approximately 2,700 NPs show that the proposed method outperforms five current state-of-art approaches in the overlapping NPs segmentation. Post-print / Final draft

This paper considers the wood species identification from images of boards. The identification using only visual features of the surface is a challenging task even for an expert. The task becomes especially difficult when the wood species are from the same family. We propose a CNN based framework for the fine-grained classification of wood species. The framework includes a patch extraction procedure where board images are divided into image patches. Each patch is separately classified using the CNN resulting in multiple classification results per board. Finally, the patch classification results for a single board are combined. We evaluate various CNN architectures using the challenging data, consisting of species from the Pinaceae family. In addition, we propose three alternative decision rules for combining the patch classification results. By selecting a suitable amount of image patches, the proposed framework was able to achieve over 99% identification accuracy and real-time performance. Post-print / Final draft

The quality control of timber products is vital for the sawmill industry pursuing more efficient production processes. This paper considers the automatic detection of mechanical damages in wooden board surfaces occurred during the sawing process. Due to the high variation in the appearance of the mechanical damages and the presence of several other surface defects on the boards, the detection task is challenging. In this paper, an efficient convolutional neural network based framework that can be trained with a limited amount of annotated training data is proposed. The framework includes a patch extraction step to produce multiple training samples from each damaged region in the board images, followed by the patch classification and damage localization steps. In the experiments, multiple network architectures were compared: the VGG-16 architecture achieved the best results with over 92% patch classification accuracy and it enabled accurate localization of the mechanical damages. Post-print / Final draft

Tracking timber in the sawmill environment from the raw material (logs) to the end product (boards) provides various benefits including efficient process control, the optimization of sawing, and the prediction of end-product quality. In practice, the tracking of timber through the sawmilling process requires a methodology for tracing the source of materials after each production step. The tracing is especially difficult through the actual sawing step where a method is needed for identifying from which log each board comes from. In this paper, we propose an automatic method for board identification (board-to-log matching) using the existing sensors in sawmills and multimodal encoder-decoder networks. The method utilizes point clouds from laser scans of log surfaces and grayscale images of boards. First, log surface heightmaps are generated from the point clouds. Then both the heightmaps and board images are converted into ”barcode” images using convolutional encoder-decoder networks. Finally, the ”barcode” images are utilized to find matching logs for the boards. In the experimental part of the work, different encoderdecoder architectures were evaluated and the effectiveness of the proposed method was demonstrated using challenging data collected from a real sawmill. Post-print / Final draft

Dance is an icon of human expression. Despite astounding diversity around the world’s cultures and dazzling abundance of reminiscent animal systems, the evolution of dance in the human clade remains obscure. Dance requires individuals to interactively synchronize their whole-body tempo to their partner’s, with near-perfect precision. This capacity is motorically-heavy, engaging multiple neural circuitries, but also dependent on an acute socio-emotional bond between partners. Hitherto, these factors helped explain why no dance forms were present amongst nonhuman primates. Critically, evidence for conjoined full-body rhythmic entrainment in great apes that could help reconstruct possible proto-stages of human dance is still lacking. Here, we report an endogenously-effected case of ritualized dance-like behaviour between two captive chimpanzees – synchronized bipedalism. We submitted video recordings to rigorous time-series analysis and circular statistics. We found that individual step tempo was within the genus’ range of “solo” bipedalism. Between-individual analyses, however, revealed that synchronisation between individuals was non-random, predictable, phase concordant, maintained with instantaneous centi-second precision and jointly regulated, with individuals also taking turns as “pace-makers”. No function was apparent besides the behaviour’s putative positive social affiliation. Our analyses show a first case of spontaneous whole-body entrainment between two ape peers, thus providing tentative empirical evidence for phylogenies of human dance. Human proto-dance, we argue, may have been rooted in mechanisms of social cohesion among small groups that might have granted stress-releasing benefits via gait-synchrony and mutual-touch. An external sound/musical beat may have been initially uninvolved. We discuss dance evolution as driven by ecologically-, socially- and/or culturally-imposed “captivity”.

This chapter summarizes the current state of the art in psychology and neuroscience of music-related emotions by examining one particularly problematic yet common emotion, sadness. Three controversial yet eminent theoretical constructs from the field of affective sciences are covered, namely affect dimensions, esthetic emotions and the role of body in emotion induction. A hierarchical framework for constructing emotional experiences is proposed to solve the discrepant and incompatible observations in the topic. Sadness in music demonstrates the way core affects, basic emotions and complex esthetic experiences are mapped with different mechanisms and levels of explanation.

Acoustic and musical components of consonance and dissonance perception have been recently identified. This study expands the range of predictors of consonance and dissonance by three analytical operations. In Experiment 1, we identify the underlying structure of a number of central predictors of consonance and dissonance extracted from an extensive dataset of chords using a hierarchical cluster analysis. Four feature categories are identified largely confirming the existing three categories (roughness, harmonicity, familiarity), including spectral envelope as an additional category separate from these. In Experiment 2, we evaluate the current model of consonance/dissonance by Harrison and Pearce by an analysis of three previously published datasets. We use linear mixed models to optimize the choice of predictors and offer a revised model. We also propose and assess a number of new predictors representing familiarity. In Experiment 3, the model by Harrison and Pearce and our revised model are evaluated with nine datasets that provide empirical mean ratings of consonance and dissonance. The results show good prediction rates for the Harrison and Pearce model (62%) and a still significantly better rate for the revised model (73%). In the revised model, the harmonicity predictor of Harrison and Pearce’s model is replaced by Stolzenburg’s model, and a familiarity predictor coded through a simplified classification of chords replaces the original corpus-based model. The inclusion of spectral envelope as a new category is a minor addition to account for the consonance/dissonance ratings. With respect to the anatomy of consonance/dissonance, we analyze the collinearity of the predictors, which is addressed by principal component analysis of all predictors in Experiment 3. This captures the harmonicity and roughness predictors into one component; overall, the three components account for 66% of the consonance/dissonance ratings, where the dominant variance explained comes from familiarity (46.2%), followed by roughness/harmonicity (19.3%).

The quality of the end product in the sawmill industry is highly dependent on the distribution of knots. If the internal structure of the logs used as raw material were known it would be possible to optimize the sawing process by controlling the locations of individual knots in the resulting boards. Methods such as Computer Tomography or Magnetic Resonance Imaging can be used to gain reliable information about the internal structure of the logs prior to sawing. Such scanners are, however, expensive or slow, which renders the use of these methods ill-suited to online integration in sawmill operations. Laser range scanners, on the other hand, are very fast but provide only external information. A method to estimate the internal log structure using only external information provided by a laser range scanner is proposed in this paper. The method comprises five major steps: point cloud filtering and centreline estimation, log surface heightmap generation, knot segmentation, volumetric reconstruction of knots and virtual sawing. The end result is the generation of images of virtual boards for the given sawing parameters. The method was evaluated on debarked softwood logs. The experimental part of the work demonstrates that the pixel intensities of the virtual boards generated using the proposed method correlate well with the probabilities of knots appearing in the corresponding locations in the final product. Virtual sawing allows exhaustive evaluation of different sawing parameters, thus making it possible to optimize sawing parameters prior to sawing.

Material flow characterization is important in the process industries and its further automation. In this study, close-to-laminar pulp suspension flows are analyzed based on double-exposure images captured in laboratory conditions. The correlation-based methods including autocorrelation and the particle image pattern technique were studied. During the experiments, synthetic and real test data with manual ground truth were used. The particle image pattern matching method showed better performance achieving the accuracy of 90.0% for the real data set with linear motion of the suspension and 79.2% for the data set with flow distortions.

After decades of research, it remains unclear whether emotion lateralization occurs because one hemisphere is dominant for processing the emotional content of the stimuli, or whether emotional stimuli activate lateralised networks associated with the subjective emotional experience. By using emotion-induction procedures, we investigated the effect of listening to happy and sad music on three well-established lateralization tasks. In a prestudy, Mozart's piano sonata (K. 448) and Beethoven's Moonlight Sonata were rated as the most happy and sad excerpts, respectively. Participants listened to either one emotional excerpt, or sat in silence before completing an emotional chimeric faces task (Experiment 1), visual line bisection task (Experiment 2) and a dichotic listening task (Experiment 3 and 4). Listening to happy music resulted in a reduced right hemispheric bias in facial emotion recognition (Experiment 1) and visuospatial attention (Experiment 2) and increased left hemispheric bias in language lateralization (Experiments 3 and 4). Although Experiments 1-3 revealed an increased positive emotional state after listening to happy music, mediation analyses revealed that the effect on hemispheric asymmetries was not mediated by music-induced emotional changes. The direct effect of music listening on lateralization was investigated in Experiment 4 in which tempo of the happy excerpt was manipulated by controlling for other acoustic features. However, the results of Experiment 4 made it rather unlikely that tempo is the critical cue accounting for the effects. We conclude that listening to music can affect functional cerebral asymmetries in well-established emotional and cognitive laterality tasks, independent of music-induced changes in the emotion state.

It has been repeatedly shown that sad music induces mainly pleasant or mixed emotions, and is particularly relevant for self-regulation goals. However, this is not entirely compatible with the view that sadness is one of the basic emotions experienced in the face of an unpleasant event or a loss. Also, a distinction between grief and sadness is often drawn, which seemingly does not have relevance in relation to musical experiences. The discrepancy between the positive accounts of emotions associated with sad music and those present in ordinary sadness may be related to the previously unacknowledged spectrum of affects associated with music-related sadness. The present study aims to expose the underlying affective experiences of music-related sadness. To examine this, a large qualitative data, consisting of open-ended answers from 363 participants, was subjected to thematic content analysis. The analysis revealed a range of emotions experienced which were classified into three themes: Grief, Melancholia, and Sweet sorrow. These themes differed depending on the valence of the overall experience and the contextual aspects. In addition, emotion induction mechanisms distinguished the themes and several previously unidentified types of affect regulation were observed. Variations in the ways people conceptualise sadness and music lead to differences in the affect regulation processes. In contrast to past research, the results suggest that truly negative emotions are relevant in association with music-related sadness. Dividing the music-evoked sadness into different categories of affective experiences helps to explain the current discrepancies and paradoxes surrounding sadness and music.

Emotion-related areas of the brain, such as the medial frontal cortices, amygdala, and striatum, are activated during listening to sad or happy music as well as during listening to pleasurable music. Indeed, in music, like in other arts, sad and happy emotions might co-exist and be distinct from emotions of pleasure or enjoyment. Here we aimed at discerning the neural correlates of sadness or happiness in music as opposed those related to musical enjoyment. We further investigated whether musical expertise modulates the neural activity during affective listening of music. To these aims, 13 musicians and 16 non-musicians brought to the lab their most liked and disliked musical pieces with a happy and sad connotation. Based on a listening test, we selected the most representative 18 sec excerpts of the emotions of interest for each individual participant. Functional magnetic resonance imaging (fMRI) recordings were obtained while subjects listened to and rated the excerpts. The cortico-thalamo-striatal reward circuit and motor areas were more active during liked than disliked music, whereas only the auditory cortex and the right amygdala were more active for disliked over liked music. These results discern the brain structures responsible for the perception of sad and happy emotions in music from those related to musical enjoyment. We also obtained novel evidence for functional differences in the limbic system associated with musical expertise, by showing enhanced liking-related activity in fronto-insular and cingulate areas in musicians.

Music is often associated with the emotions of nostalgia and longing. According to previous survey studies both nostalgia and longing are among the most commonly evoked emotions by music (Juslin, 2011). Despite nostalgia’s significance as a musical emotion, research on the specific properties of music that might contribute to this particular emotion has been scarce. A recent empirical experiment by Lahdelma and Eerola (2014) sought to explore whether single chords could be effective at conveying musical emotions to listeners, which spanned complex emotions such as nostalgia. According to the results single chords such as the minor triad, the minor seventh and the major seventh communicate the emotion of nostalgia effectively. The aim of the current paper is to raise several possible explanations that might account for single chords’ ability to convey the emotion of nostalgia. In these explanations we consider cultural, music-theoretical and psychoacoustic issues, as well as their possible interactions. The three proposed candidate explanations are (1) learning, (2) intrinsic emotional connotations arising from tonal relations, and (3) clashing conventions arising from concurrent yet separate affective associations, stemming from certain triad and interval combinations. Finally, we propose experimental designs for future research to empirically test these explanations.

Segmentation of partially overlapping objects with a known shape is needed in an increasing amount of various machine vision applications. This paper presents a method for segmentation of clustered partially overlapping objects with a shape that can be approximated using an ellipse. The method utilizes silhouette images, which means that it requires only that the foreground (objects) and background can be distinguished from each other. The method starts with seedpoint extraction using bounded erosion and fast radial symmetry transform. Extracted seedpoints are then utilized to associate edge points to objects in order to create contour evidence. Finally, contours of the objects are estimated by fitting ellipses to the contour evidence. The experiments on one synthetic and two different real data sets showed that the proposed method outperforms two current state-of-art approaches in overlapping objects segmentation.

Segmentation of overlapping convex objects has various applications, for example, in nanoparticles and cell imaging. Often the segmentation method has to rely purely on edges between the background and foreground making the analyzed images essentially silhouette images. Therefore, to segment the objects, the method needs to be able to resolve the overlaps between multiple objects by utilizing prior information about the shape of the objects. This paper introduces a novel method for segmentation of clustered partially overlapping convex objects in silhouette images. The proposed method involves three main steps: pre-processing, contour evidence extraction, and contour estimation. Contour evidence extraction starts by recovering contour segments from a binarized image by detecting concave points. After this, the contour segments which belong to the same objects are grouped. The grouping is formulated as a combinatorial optimization problem and solved using the branch and bound algorithm. Finally, the full contours of the objects are estimated by a Gaussian process regression method. The experiments on a challenging dataset consisting of nanoparticles demonstrate that the proposed method outperforms three current state-of-art approaches in overlapping convex objects segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have a convex shape., Comment: 16 pages, 11 Figures

Increased age has been found to be associated with differences in musical preferences in adulthood. In past research, these differences were mostly attributed to changes in the social context. However, these influences were small and a large proportion of variance in age trends in musical preferences still remains to be explained. The aim of this article is to investigate the hypothesis that age trends in musical preferences are related to differences in preferences for some intrinsic attributes of the music in line with the Music Preferences in Adulthood Model (Bonneville-Roussy et al., 2017). Adult participants ( N = 481) were asked to rate their preferences for extracts of 51 audio-music recordings (music clips) and musical attributes related to dynamics, pitch, structure, tempo, and timbre. Audio-features of the 51 clips were extracted using Music Information Retrieval methods. Using self-report, we found that the musical preferences of adults were linked with distinct likings for musical attributes, with large effects. We also discovered that self-rated attributes associated with dynamics and timbre moderated the links between age and musical preferences. Using the extracted features, we found that musical preferences were linked with distinct patterns of musical features. Finally, we established that the patterns of preferences of emerging, young and middle-aged adults were increasingly influenced by audio-features of timbre, dynamics and tonal clarity. These findings suggest that age trends in musical preferences could be partially explained by differences in the ways individuals process the intrinsic attributes of the music with age.

Counting and tracking fish populations is important for conservation purposes as well as for the fishing industry. Various non-invasive automatic fish counters exist based on such principles as resistivity, light beams and sonar. However, such methods typically cannot make distinction between fish and other passing objects, and moreover, cannot recognize different species. Computer vision techniques provide an attractive alternative for building a more robust and versatile fish counting systems. In this paper we present the fish detection framework for noisy videos captured in water with low visibility. For this purpose, we compare three background subtraction methods for the task. Moreover, we propose necessary post-processing steps and heuristics to detect the fish and separate them from other moving objects. The results showed that by choosing an appropriate background subtraction method, it is possible to achieve a satisfying detection accuracy of 80% and 60% for two challenging datasets. The proposed method will form a basis for the future development of fish species identification methods.