Sound and Emotions

There is no exact model for the relationship between the autonomic nervous system (ANS) and evoked or perceived emotion. Music has long been a privileged field for exploration, while the contribution of soundscape research is more recent. It is known that health is influenced by the sonic environment, and the study here presented aimed to investigate the nature and strength of relationships between soundscape features and physiological responses linked to relaxation or stress. In a controlled experiment, seventeen healthy volunteers moved freely inside a physical installation listening to soundscape recordings of nature, urban parks, eateries, and shops, reproduced using 3D ambisonic techniques. Physiological responses were continuously captured, then detrended, downsampled, and analysed with multivariate linear regression onto orthogonal acoustic and perceptual stimuli features that had been previously determined. Measures of Peripheral Temperature regressed onto SoundMass, an acoustic feature, and onto Calm-to-Chaotic, a perceptual feature, in each case with a moderately sized effect. A smaller effect was found for Heart Rate onto VariabilityFocus, an acoustic feature, and for Skin Conductance onto the interaction between the acoustic features. These relationships could be coherently accounted for by neurophysiological theory of how ANS activation leads to emotional relaxation or stress. We discuss limitations of the present study and considerations for future soundscape emotion research, as well as more immediate practical implications.

This paper examines the induction of emotions while listening to Romantic orchestral music. The study seeks to explore the relationship between subjective ratings of felt emotion and acoustic and physiological features. We employed 75 musical excerpts as stimuli to gather responses of excitement and pleasantness from 20 participants. During the experiments, physiological responses of the participants were measured, including blood volume pulse (BVP), skin conductance (SC), respiration rate (RR) and facial electromyography (EMG). A set of acoustic features was derived related to dynamics, harmony, timbre and rhythmic properties of the music stimuli. Based on the measured physiological signals, a set of physiological features was also extracted. The feature extraction process is discussed with particular emphasis on the interaction between acoustical and physiological parameters. Statistical relations among audio, physiological features and emotional ratings from psychological experiments were systematically investigated. Finally, a forward step-wise multiple linear regression model (MLR) was employed using the best features, and its prediction efficiency was evaluated and discussed. The results indicate that merging acoustic and physiological modalities substantially improves prediction of participants' ratings of felt emotion compared to the results using the modalities in isolation.

Music is not only a set of sounds, it evokes emotions, subjectively perceived by listeners. The growing amount of audio data available on CDs and in the Internet wakes up a need for content-based searching through these files. The user may be interested in finding pieces in a specific mood. The goal of this paper is to elaborate tools for such a search. A method for the appropriate objective description (parameterization) of audio files is proposed, and experiments on a set of music pieces are described. The results are summarized in concluding chapter.

Emotion recognition from generalized sounds is an interdisciplinary and emerging field of research. A vital requirement for this kind of investigations is the availability of ground truth datasets. Currently, there are 2 freely available datasets of emotionally annotated sounds, which, however, do not include sound evenets (SEs) with manifestation of the spatial location of the source. The latter is an inherent natural component of SEs, since all sound sources in real-world conditions are physically located and perceived somewhere in the listener's surrounding space. In this work we present a novel emotionally annotated sounds dataset consisting of 32 SEs that are spatially rendered using appropriate binaural processing. All SEs in the dataset are available in 5 spatial positions corresponding to source/receiver angles equal to 0, 45, 90, 135 and 180 degrees. We have used the IADS dataset as the initial collection of SEs prior to binaural processing. The annotation measures obtained for the novel binaural dataset demonstrate a significant accordance with the existing IADS dataset, while small ratings declinations illustrate a perceptual adaptation imposed by the more realistic SEs spatial representation.

ARTE270 is an innovative experimental platform that combines panoramic optical projection (video and photography) with surround sound. The idea is to immerse the spectator/listener into audiovisual content both in realistic and artistic terms. It initially emerged in order to perform artistic experimentation on data derived from the flourishing natural environment of the Ionian Islands. Nevertheless, in the course of time, it comes up that a very important potential application lies with sustainable development. Our platform has been tried as part of a holistic approach for capturing, preserving and promoting further forms of cultural heritage as well as cultural networking through the new rendering capabilities of digital media.

A variety of recent researches in Audio Emotion Recognition (AER) outlines high performance and retrieval accuracy results. However, in most works music is considered as the original sound content that conveys the identified emotions. One of the music characteristics that is found to represent a fundamental means for conveying emotions are the rhythmrelated acoustic cues. Although music is an important aspect of everyday life, there are numerous non-linguistic and nonmusical sounds surrounding humans, generally defined as sound events (SEs). Despite this enormous impact of SEs to humans, a scarcity of investigations regarding AER from SEs is observed. There are only a few recent investigations concerned with SEs and AER, presenting a semantic connection between the former and the listener's triggered emotion. In this work we analytically investigate the connection of rhythm-related characteristics of a wide range of common SEs with the arousal of the listener using sound events with semantic content. To this aim, several feature evaluation and classification tasks are conducted using different ranking and classification algorithms. High accuracy results are obtained, demonstrating a significant relation of SEs rhythmic characteristics to the elicited arousal.

Emotion recognition from sound signals represents an emerging field of recent research. Although many existing works focus on emotion recognition from music, there seems to be a relative scarcity of research on emotion recognition from general sounds. One of the key characteristics of sound events is the sound source spatial position, i.e. the location of the source relatively to the acoustic receiver. Existing studies that aim to investigate the relation of the latter source placement and the elicited emotions are limited to distance, front and back spatial localization and/or specific emotional categories. In this paper we analytically investigate the effect of the source angular position on the listener's emotional state, modeled in the well-established valence/arousal affective space. Towards this aim, we have developed an annotated sound events dataset using binaural processed versions of the available International Affective Digitized Sound (IADS) sound events library. All subjective affective annotations were obtained using the Self Assessment Manikin (SAM) approach. Preliminary results obtained by processing these annotation scores are likely to indicate a systematic change in the listener affective state as the sound source angular position changes. This trend is more obvious when the sound source is located outside of the visible field of the listener.

Sound events can carry multiple information, related to the sound source and to ambient environment. However, it is well-known that sound evokes emotions, a fact that is verified by works in the disciplines of Music Emotion Recognition and Music Information Retrieval that focused on the impact of music to emotions. In this work we introduce the concept of affective acoustic ecology that extends the above relation to the general concept of sound events. Towards this aim, we define sound event as a novel audio structure with multiple components. We further investigate the application of existing emotion models employed for music affective analysis to sonic, non-musical, content. The obtained results indicate that although such application is feasible, no significant trends and classification outcomes are observed that would allow the definition of an analytic relation between the technical characteristics of a sound event waveform and raised emotions.

Sound events are proven to have an impact on the emotions of the listener. Recent works on the field of emotion recognition from sound events show, on one hand, the possibility of automatic emotional information retrieval from sound events and, on the other hand, the need for deeper understanding of the significance of the sound events' semantic content on listener's affective state. In this work we present a first, to the best of authors' knowledge, investigation of the relation between the semantic similarity of the sound events and the elicited emotion. For that cause we use two emotionally annotated sound datasets and the Wu-Palmer semantic similarity measure according to WordNet. Results indicate that the semantic content seems to have a limited role in the conformation of the listener's affective states. On the contrary, when the semantic content is matched to specific areas in the Arousal-Valence space or also the source's spatial position is taken into account, it is exhibited that the importance of the semantic content effect is higher, especially for the cases with medium to low valence and medium to high arousal or when the sound source is at the lateral positions of the listener's head, respectively.

Auralisation is becoming more commonplace as a means of communicating and disseminating plans for changes in our environmental landscape due to major infrastructure projects such as road, rail and airport developments, where noise can be a significant issue. This paper presents an approach to this challenge in the context of a major public consultation for a UK road project as well as commenting on different means for facilitating presentation of, and engagement with, both audio and visual material. Although such methods are generally used for information only, every effort is made to ensure that the final result is both plausible and comparable to existing on-site conditions, and calibrated accordingly, with a view to supporting other more standardised methods and metrics. However, it is also possible to develop more quantitative measures as to how effective and successful such auralisations are in terms of conveying a representation of a given scene, and any planned for interventions. In this work the Self Assessment Manikin and soundscape classification descriptors are used in a series of listening tests to better understand the approach that has been adopted for these types of projects.

Emotion recognition from sound signals represents an emerging field of recent research. Although many existing works focus on emotion recognition from music, there seems to be a relative scarcity of research on emotion recognition from general sounds. One of the key characteristics of sound events is the sound source spatial position, i.e. the location of the source relatively to the acoustic receiver. Existing studies that aim to investigate the relation of the latter source placement and the elicited emotions are limited to distance, front and back spatial localization and/or specific emotional categories. In this paper we analytically investigate the effect of the source angular position on the listener's emotional state, modeled in the well-established valence/arousal affective space. Towards this aim, we have developed an annotated sound events dataset using binaural processed versions of the available International Affective Digitized Sound (IADS) sound events library. All subjective affective annotations were obtained using the Self Assessment Manikin (SAM) approach. Preliminary results obtained by processing these annotation scores are likely to indicate a systematic change in the listener affective state as the sound source angular position changes. This trend is more obvious when the sound source is located outside of the visible field of the listener.

Emotion recognition from generalized sounds is an interdisciplinary and emerging field of research. A vital requirement for this kind of investigations is the availability of ground truth datasets. Currently, there are 2 freely available datasets of emotionally annotated sounds, which, however, do not include sound evenets (SEs) with manifestation of the spatial location of the source. The latter is an inherent natural component of SEs, since all sound sources in real-world conditions are physically located and perceived somewhere in the listener's surrounding space. In this work we present a novel emotionally annotated sounds dataset consisting of 32 SEs that are spatially rendered using appropriate binaural processing. All SEs in the dataset are available in 5 spatial positions corresponding to source/receiver angles equal to 0, 45, 90, 135 and 180 degrees. We have used the IADS dataset as the initial collection of SEs prior to binaural processing. The annotation measures obtained for the novel binaural dataset demonstrate a significant accordance with the existing IADS dataset, while small ratings declinations illustrate a perceptual adaptation imposed by the more realistic SEs spatial representation.

Emotion labels are usually obtained via either manual annotation, which is tedious and time-consuming, or questionnaires, which neglect the time-varying nature of emotions and depend on human's unreliable introspection. To overcome these limitations, we developed a continuous, real-time, joystick-based emotion annotation framework. To assess the same, 30 subjects each watched 8 emotion-inducing videos. They were asked to indicate their instantaneous emotional state in a valence-arousal (V-A) space, using a joystick. Subsequently, five analyses were undertaken: (i) a System Usability Scale (SUS) questionnaire unveiled the framework's excellent usability; (ii) MANOVA analysis of the mean V-A ratings and (iii) trajectory similarity analyses of the annotations confirmed the successful elicitation of emotions; (iv) Change point analysis of the annotations, revealed a direct mapping between emotional events and annotations, thereby enabling automatic detection of emotionally salient points in the videos; and (v) Support Vector Machines (SVM) were trained on classification of 5 second chunks of annotations as well as their change-points. The classification results confirmed that ratings patterns were cohesive across the participants. These analyses confirm the value, validity, and usability of our annotation framework. They also showcase novel tools for gaining greater insights into the emotional experience of the participants.

Music is frequently regarded as a unique way to connect with dementia patients. Yet little is known about how persons with dementia respond emotionally to music. Are their responses different from those of healthy listeners? If so, why? The present study makes a first attempt to tackle these issues in a Portuguese context, with a focus on psychological mechanisms. In Experiment 1, featuring 20 young and healthy adults, we found that musical excerpts which have previously been shown to activate specific emotion induction mechanisms (brain stem reflex, contagion, episodic memory, musical expectancy) in Sweden were valid and yielded predicted emotions also in Portugal, as indexed by self-reported feelings, psychophysiology, and post hoc mechanism indices. In Experiment 2, we used the same stimuli to compare the responses of 20 elderly listeners diagnosed with Alzheimer’s disease (AD) with those of 20 healthy listeners. We controlled for cognitive functioning (Mini-Mental State Examinat...

Emotion recognition from sound signals represents an emerging field of recent research. Although many existing works focus on emotion recognition from music, there seems to be a relative scarcity of research on emotion recognition from general sounds. One of the key characteristics of sound events is the sound source spatial position, i.e. the location of the source relatively to the acoustic receiver. Existing studies that aim to investigate the relation of the latter source placement and the elicited emotions are limited to distance, front and back spatial localization and/or specific emotional categories. In this paper we analytically investigate the effect of the source angular position on the listener's emotional state, modeled in the well-established valence/arousal affective space. Towards this aim, we have developed an annotated sound events dataset using binaural processed versions of the available International Affective Digitized Sound (IADS) sound events library. All subjective affective annotations were obtained using the Self Assessment Manikin (SAM) approach. Preliminary results obtained by processing these annotation scores are likely to indicate a systematic change in the listener affective state as the sound source angular position changes. This trend is more obvious when the sound source is located outside of the visible field of the listener.

Emotion recognition from sound signals represents an emerging field of recent research. Although many existing works focus on emotion recognition from music, there seems to be a relative scarcity of research on emotion recognition from general sounds. One of the key characteristics of sound events is the sound source spatial position, i.e. the location of the source relatively to the acoustic receiver. Existing studies that aim to investigate the relation of the latter source placement and the elicited emotions are limited to distance, front and back spatial localization and/or specific emotional categories. In this paper we analytically investigate the effect of the source angular position on the listener’s emotional state, modeled in the well–established valence/arousal affective space. Towards this aim, we have developed an annotated sound events dataset using binaural processed versions of the available International Affective Digitized Sound (IADS) sound events library. All sub...

Perceiving Emotion in Sounds: Does Timbre Play a Role? (December 2011) Casady Diane Bowman, B.S., Oklahoma State University Chair of Advisory Committee: Dr. Takashi Yamauchi Acoustic features of sound such as pitch, loudness, perceived duration and timbre have been shown to be related to emotion in regard to sound, demonstrating that research involving the important connection between the perceived emotions and their timbres is lacking. This study investigates the relationship between acoustic features of sound and emotion with regard to timbre. In two experiments, we investigated whether particular acoustic components of sound can predict timbre and particular categories of emotion, and how these attributes are related. Two behavioral experiments related perceived emotion ratings with synthetically created sounds and International Affective Digitized Sounds (IADS). Also, two timbre experiments found a connection between acoustic components of synthetically created sounds, and IADS....

In this paper we present a status report of the design and development stages of an online social network system based on Soundscapes. Additionally, a detailed description of the auditory scene analysis module of the system is made. The term Soundscape is used to describe the relation and interaction between the acoustic environment of a place and its inhabitants, namely on how these perceive and judge the sound. From this premise we developed a mobile application that senses and shares information regarding the Soundscapes of the places inhabited by each user during his/her daily life. Thus, each user acts as a terminal of a sensor network linked through the use of sound. The goal of this research is to assess the use of Soundscapes in social network interactions and consequently promote sound awareness among people.

In this paper we present a status report of the design and development stages of an online social network system based on Soundscapes. Additionally, a detailed description of the auditory scene analysis module of the system is made. The term Soundscape is used to describe the relation and interaction between the acoustic environment of a place and its inhabitants, namely on how these perceive and judge the sound. From this premise we developed a mobile application that senses and shares information regarding the Soundscapes of the places inhabited by each user during his/her daily life. Thus, each user acts as a terminal of a sensor network linked through the use of sound. The goal of this research is to assess the use of Soundscapes in social network interactions and consequently promote sound awareness among people.

Sonification is the utilization of sounds to convey information about data or events. There are two types of emotions associated with sounds: (1) “perceived” emotions, in which listeners recognize the emotions expressed by the sound, and (2) “induced” emotions, in which listeners feel emotions induced by the sound. Although listeners may widely agree on the perceived emotion for a given sound, they often do not agree about the induced emotion of a given sound, so it is difficult to model induced emotions. This paper describes the development of several machine and deep learning models that predict the perceived and induced emotions associated with certain sounds, and it analyzes and compares the accuracy of those predictions. The results revealed that models built for predicting perceived emotions are more accurate than ones built for predicting induced emotions. However, the gap in predictive power between such models can be narrowed substantially through the optimization of the ma...

Technologies capable of automatically sensing and recognizing emotion are becoming increasingly prevalent in performance and compositional practice. Though these technologies are complex and diverse, we present a typology that draws on similarities with computational systems for expressive music performance. This typology provides a framework to present results from the development of two audio environments for the Emotional Imaging Composer, a commercial product for realtime arousal/valence recognition that uses signals from the autonomic nervous system. In the first environment, a spectral delay processor for live vocal performance uses the performer's emotional state to interpolate between subspaces of the arousal/valence plane. For the second, a sonification mapping communicates continuous arousal and valence measurements using tempo, loudness, decay, mode, and roughness. Both were informed by empirical research on musical emotion, though differences in desired output schemas manifested in different mapping strategies.

Subjective and psychophysiological emotional responses to music from two different cultures were compared within these two cultures. Two identical experiments were conducted: the first in the Congolese rainforest with an isolated population of Mebenzélé Pygmies without any exposure to Western music and culture, the second with a group of Western music listeners, with no experience with Congolese music. Forty Pygmies and 40 Canadians listened in pairs to 19 music excerpts of 29-99 s in duration in random order (eight from the Pygmy population and 11 Western instrumental excerpts). For both groups, emotion components were continuously measured: subjective feeling (using a two-dimensional valence and arousal rating interface), peripheral physiological activation, and facial expression. While Pygmy music was rated as positive and arousing by Pygmies, ratings of Western music by Westerners covered the range from arousing to calming and from positive to negative. Comparing psychophysiological responses to emotional qualities of Pygmy music across participant groups showed no similarities. However, Western stimuli, rated as high and low arousing by Canadians, created similar responses in both participant groups (with high arousal associated with increases in subjective and physiological activation). Several low-level acoustical features of the music presented (tempo, pitch, and timbre) were shown to affect subjective and physiological arousal similarly in both cultures. Results suggest that while the subjective dimension of emotional valence might be mediated by cultural learning, changes in arousal might involve a more basic, universal response to low-level acoustical characteristics of music.

The close relationship between music and emotions seems to be a commonplace. Although numerous attempts have been made to better understand this relationship, however, there are still many unresolved questions regarding the manifold connections between these two human phenomena. Our panel wants to approach the relationship between music and emotions by understanding both – music and emotion – as expressions, and therefore as being very similar in character. Thereby the voice acts as link between music and emotions, since it is used expressively in both the musical (singing, declaming etc) as well as in the emotional realm (scream, shout, laugh etc) and thus is building a bridge between both the aesthetical and the affective side.
The panel wants to examine the relationship between music, voice and emotions from as manifold perspectives as possible. Thematic areas could include performance studies, aesthetical concepts, analysis or history of compositions, aspects of embodiment. Thereby, we are particularly interested to contrast examples that stem from different historical as well as different cultural areas and also invite proposals concerning the border between vocal sounds in music and vocal sounds in everyday life. The aim of the panel is to combine as manifold aspects of musical and vocal expressions of emotions and to thereby come as close as possible to the relationship of music and emotions in past and present.

ARTE270 is an innovative experimental platform that combines panoramic optical projection (video and photography) with surround sound. The idea is to immerse the spectator/listener into audiovisual content both in realistic and artistic terms. It initially emerged in order to perform artistic experimentation on data derived from the flourishing natural environment of the Ionian Islands. Nevertheless, in the course of time, it comes up that a very important potential application lies with sustainable development. Our platform has been tried as part of a holistic approach for capturing, preserving and promoting further forms of cultural heritage as well as cultural networking through the new rendering capabilities of digital media.

Sound events are proven to have an impact on the emotions of the listener. Recent works on the field of emotion recognition from sound events show, on one hand, the possibility of automatic emotional information retrieval from sound events and, on the other hand, the need for deeper understanding of the significance of the sound events’ semantic content on listener’s affective state. In this work we present a first, to the best
of authors’ knowledge, investigation of the relation between the semantic similarity of the sound events and the elicited emotion. For that cause we use two emotionally annotated sound datasets and the Wu-Palmer semantic similarity measure according to WordNet. Results indicate that the semantic content seems to have a limited role in the conformation of the listener’s affective states. On the contrary, when the semantic content is matched to specific areas in the Arousal - Valence space or also the source’s spatial position is taken into account, it is exhibited that the importance of the semantic content effect is higher, especially for the cases with medium to low valence and medium to high arousal or when the sound source is at the lateral positions of the listener’s head, respectively.

“Verbis defectis musica incipit” that’s Latin for, “Music takes us where word cannot.” Music is an important part of our life. There is a strong relationship between music and human mind. In most researchers’ opinion, people value music primarily because of the emotions it evokes. As Juslin says “People use music to change emotions, to release emotions, to match their current emotion, to enjoy or comforts themselves and to relieve stress”. He also represents a novel theoretical framework featuring six additional mechanisms through which music listening may induce emotions: brain stem reflexes, evaluative conditioning, emotional contagion, visual imagery, episodic memory, and musical expectancy.As listening of music varies with emotions, a better music classification is recommended to create a well-defined automated music playlist. Music usually classified according to genre and for limitation to cover these huge number of music collection sub-genre are introduced. If we go little deep while we are listening music, an artist or genre is not the primary key for music selection to satisfy our emotional state. There is two main roles go with our emotion, one is musical instrument and another one is tone. Sometimes lyrics reflect on our emotion. For example, a listeners with happy mood may want to listen ‘Don`t Worry, Be Happy’ or ‘Hotat Roud’, they will not care about the artist, they just need that specific speech to satisfy them.Weather is another factor for a music listening which we cannot realize most of the time. As we listen music to satisfy our emotion and mood states changes with weather. Different categories of weather are responsible for changing in emotions, which indicates a character either positive or negative for the organism, but these changes, varies from one to another . For example, in rainy weather a listener may want to listen a sad music to evoke his/her emotion; on other hand another one wants to a happy music. In other word we can say, weather helps us to feel our emotion more deeply.In our work, we propose a different list of music based on mood and weather. It is recommended that listening of music depends on our mood. The interesting part is that, from this model we can define the relation between human mind and weather.

Music mood describes the i nherent emotional meaning of a music clip. It is helpful in music understanding, music search and some music-related applications. In this paper, a hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures. Three feature sets , intensity, timbre and rhythm, are extracted to represent the characteristics of a music clip. Moreover, a mood tracking approach is also presented for a whole piece of music. Experimental evaluations indicate that the proposed algorithms produce satisfactory results.

Urban sonic ecology represents a major field of research interest for exploiting the relations raised through sound between human populations and a city environment. Recently, the concept of emotional city has boosted the ideas and efforts towards exploiting the particular characteristics of an urban soundscape as a means for conveying emotions. The result of these efforts is the concept of affective acoustic ecology. In this work we aim to provide a fundamental perspective towards modeling affective acoustic ecology, based on a novel definition of sound events, the primitive components of a soundscape. The proposed model considers both acoustic signal-dependent features and semantic content as the potential soundscape parameters that trigger emotions.

Music mood describes the i nherent emotional meaning of a music clip. It is helpful in music understanding, music search and some music-related applications. In this paper, a hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures. Three feature sets , intensity, timbre and rhythm, are extracted to represent the characteristics of a music clip. Moreover, a mood tracking approach is also presented for a whole piece of music. Experimental evaluations indicate that the proposed algorithms produce satisfactory results.

Emotion recognition from sound signals represents an emerging field of recent research. Although many existing works focus on emotion recognition from music, there seems to be a relative scarcity of research on emotion recognition from general sounds. One of the key characteristics of sound events is the sound source spatial position, i.e. the location of the source relatively to the acoustic receiver. Existing studies that aim to investigate the relation of the latter source placement and the elicited emotions are limited to distance, front and back spatial localization and/or specific emotional categories. In this paper we analytically investigate the effect of the source angular position on the listener’s emotional state, modeled in the well–established valence/arousal affective space. Towards this aim, we have developed an annotated sound events dataset using binaural processed versions of the available International Affective Digitized Sound (IADS) sound events library. All subjective affective annotations were obtained using the Self Assessment Manikin (SAM) approach. Preliminary results obtained by processing these annotation scores are likely to indicate a systematic change in the listener affective state as the sound source angular position changes. This trend is more obvious when the sound source is located outside of the visible field of the listener.

Emotion recognition from generalized sounds is an interdisciplinary and emerging field of research. A vital requirement for this kind of investigations is the availability of ground truth datasets. Currently, there are 2 freely available datasets of emotionally annotated sounds, which, however, do not include sound evenets (SEs) with manifestation of the spatial location of the source. The latter is an inherent natural component of SEs, since all sound sources in real-world conditions are physically located and perceived somewhere in the listener’s surrounding space. In this work we present a novel emotionally annotated sounds dataset consisting of 32 SEs that are spatially rendered using appropriate binaural processing. All SEs in the dataset are available in 5 spatial positions corresponding to source/receiver angles equal to 0, 45, 90, 135 and 180 degrees. We have used the IADS dataset as the initial collection of SEs prior to binaural processing. The annotation measures obtained for the novel binaural dataset demonstrate a significant accordance with the existing IADS dataset, while small ratings declinations illustrate a perceptual adaptation imposed by the more realistic SEs spatial representation.

A variety of recent researches in Audio Emotion Recognition (AER) outlines high performance and retrieval accuracy results. However, in most works music is considered as the original sound content that conveys the identified emotions. One of the music characteristics that is found to represent a fundamental means for conveying emotions are the rhythmrelated acoustic cues. Although music is an important aspect of everyday life, there are numerous non-linguistic and nonmusical sounds surrounding humans, generally defined as sound events (SEs). Despite this enormous impact of SEs to humans, a scarcity of investigations regarding AER from SEs is observed. There are only a few recent investigations concerned with SEs and AER, presenting a semantic connection between the former and the listener's triggered emotion. In this work we analytically investigate the connection of rhythm-related characteristics of a wide range of common SEs with the arousal of the listener using sound events with semantic content. To this aim, several feature evaluation and classification tasks are conducted using different ranking and classification algorithms. High accuracy results are obtained, demonstrating a significant relation of SEs rhythmic characteristics to the elicited arousal.

A variety of recent researches in Audio Emotion Recognition (AER) outlines high performance and retrieval accuracy results. However, in most works music is considered as the original sound content that conveys the identified emotions. One of the music characteristics that is found to represent a fundamental means for conveying emotions are the rhythm- related acoustic cues. Although music is an important aspect of everyday life, there are numerous non-linguistic and non- musical sounds surrounding humans, generally defined as sound events (SEs). Despite this enormous impact of SEs to humans, a scarcity of investigations regarding AER from SEs is observed. There are only a few recent investigations concerned with SEs and AER, presenting a semantic connection between the former and the listener’s triggered emotion. In this work we analytically investigate the connection of rhythm-related characteristics of a wide range of common SEs with the arousal of the listener using sound events with semantic content. To this aim, several feature evaluation and classification tasks are conducted using different ranking and classification algorithms. High accuracy results are obtained, demonstrating a significant relation of SEs rhythmic characteristics to the elicited arousal.

Automatic genre classification from audio has been an area of active research due to its importance in music
information retrieval systems. The clustering process in data mining is to arrange similar data into groups. The purpose of
clustering algorithm is to divide s dataset into several groups that are similar within a group is better than among groups.
The multimedia files grow day by day.
As concern of music and audio, millions of files spread every day. The automatic organization of these music or
audio files is a difficult task. In this paper, we are identifying a

Music is not only a set of sounds, it evokes emotions, subjectively perceived by listeners. The growing amount of audio data available on CDs and in the Internet wakes up a need for content-based searching through these files. The user may be interested in finding pieces in a specific mood. The goal of this paper is to elaborate tools for such a search. A method for the appropriate objective description (parameterization) of audio files is proposed, and experiments on a set of music pieces are described. The results are summarized in concluding chapter.

Music is not only a set of sounds, it evokes emotions, subjectively perceived by listeners. The growing amount of audio data available on CDs and in the Internet wakes up a need for content-based searching through these files. The user may be interested in finding pieces in a specific mood. The goal of this paper is to elaborate tools for such a search. A method for the appropriate objective description (parameterization) of audio files is proposed, and experiments on a set of music pieces are described.

Sound events can carry multiple information, related to the sound source and to ambient environment. However, it is well-known that sound evokes emotions, a fact that is verified by works in the disciplines of Music Emotion Recognition and Music Information Retrieval that focused on the impact of music to emotions. In this work we introduce the concept of affective acoustic ecology that extends the above relation to the general concept of sound events. Towards this aim, we define sound event as a novel audio structure with multiple components. We further investigate the application of existing emotion models employed for music affective analysis to sonic, non-musical, content. The obtained results indicate that although such application is feasible, no significant trends and classification outcomes are observed that would allow the definition of an analytic relation between the technical characteristics of a sound event waveform and raised emotions.

Modern interactive means combined with new digital media processing and representation technologies can provide a robust framework for enhancing user experience in multimedia entertainment systems and audiovisual artistic installations with non-traditional interaction/feedback paths based on user affective state. In this work, the ‘Elevator’ interactive audiovisual platform prototype is presented, which aims to provide a framework for signalling and expressing human behaviour related to emotions (such as anger) and finally produce a visual outcome of this behaviour, defined here as the emotional ‘thumbnail’ of the user. Optimised, real-time audio signal processing techniques are employed for monitoring the achieved anger-like behaviour, while the emotional elevation is attempted using appropriately selected combined audio/visual content reproduced using state-of-the-art audiovisual playback technologies that allow the creation of a realistic immersive audiovisual environment. The demonstration of the proposed prototype has shown that affective interaction is possible, allowing the further development of relative artistic and technological applications.

Sound events can carry multiple information, related to the sound source and to ambient environment. However, it is well-known that sound evokes emotions, a fact that is verified by works in the disciplines of Music Emotion Recognition and Music Information Retrieval that focused on the impact of music to emotions. In this work we introduce the concept of affective acoustic ecology that extends the above relation to the general concept of sound events. Towards this aim, we define sound event as a novel audio structure with multiple components. We further investigate the application of existing emotion models employed for music affective analysis to sonic, non-musical, content. The obtained results indicate that although such application is feasible, no significant trends and classification outcomes are observed that would allow the definition of an analytic relation between the technical characteristics of a sound event waveform and raised emotions.

The purpose of this study was to investigate the effects of the valence and arousal dimensions of music over the time course of physiological (skin conductance level and heart rate) and subjective (Subjective Unit of Discomfort score) recovery from an acute stressor. Participants experienced stress after being told to prepare a speech, and were then exposed to happy, peaceful, sad, or agitated music. Music with a positive valence promoted both subjective and physiological recovery better than music with a negative valence, and low-arousal music was more effective than high-arousal music. Repeated measures analyses found that the emotion conveyed by the music affected skin conductance level recovery immediately following the stressor, whereas it affected heart rate recovery in a more sustained fashion. Follow-up tests found that positively valenced low-arousal (i.e., peaceful) music was more effective across the time course than an emotionally neutral control (white noise).