Search results for: music genre classification

Authors: Roni Granot, Erica Polini

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Music is often described in terms of non-auditory adjectives such as a rising melody, a bright sound, or a zigzagged contour. Such cross modal associations have been studied with simple isolated musical parameters, but only rarely in rich musical contexts. The current study probes cross sensory associations with polarity based dimensions by means of pairings of 10 adjectives: blunt-sharp, relaxed-tense, heavy-light, low (in space)-high, low (pitch)-high, big-small, hard-soft, active-passive, bright-dark, sad-happy. 30 participants (randomly assigned to one of two groups) were asked to rate one of 27 short saxophone improvisations on a 1 to 6 scale where 1 and six correspond to the opposite pole of each dimension. The 27 improvisations included three exemplars for each of three dimensions (size, brightness, sharpness), played by three different players. Here we focus on the question of whether ratings of scales corresponding with the musical dimension were consistently rated as such (e.g. music improvised to represent a white circle rated as bright in contrast with music improvised to represent a dark circle rated as dark). Overall the average scores by dimension showed an upward trend in the equivalent verbal scale, with a low rating for small, bright and sharp musical improvisations and higher scores for large, dark and blunt improvisations. Friedman tests indicate a statistically significant difference for brightness (χ2 (2) = 19.704, p = .000) and sharpness dimensions (χ2 (2) = 15.750, p = .000), but not for size (χ2 (2) = 1.444, p = .486). Post hoc analysis with Wilcoxon signed-rank tests within the brightness dimension, show significant differences among all possible parings resulted in significant differences: the rankings of 'bright' and 'dark' (Z = -3.310, p = .001), of 'bright' and 'medium' (Z = -2.438, p = .015) and of 'dark' and 'medium' music (Z = -2.714, p = .007); but only differences between the extreme contrasts within the sharpness dimension : 'sharp' and 'blunt' music (Z = -3.147, p = .002) and between 'sharp' and 'medium' music rated on the sharpness scale (Z = - 3.054, p = .002), but not between 'medium' and 'blunt' music (Z = -.982, p = .326). In summary our study suggests a privileged link between music and the perceptual and semantic domain of brightness. In contrast, size seems to be very difficult to convey in music, whereas sharpness seems to be mapped onto the two extremes (sharp vs. blunt) rather than continuously. This is nicely reflected in the musical literature in titles and texts which stress the association between music and concepts of light or darkness rather than sharpness or size.

Keywords: audiovisual, brightness, cross-modal perception, cross-sensory correspondences, size, visual angularity

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Authors: Gehad S. Kaseb, Mona F. Ahmed

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Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. Few studies apply SA to Arabic dialects. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-AATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, classification, sentiment analysis, tweets

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Jaiden X. Schraut

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Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.

Keywords: chest X-ray, deep learning, image segmentation, image classification

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: M. Karthigayan, M. Rizon, Sazali Yaacob, R. Nagarajan, M. Muthukumaran, Thinaharan Ramachandran, Sargunam Thirugnanam

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In this paper, using a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA) are applied to the lip and eye features to classify the human emotions. Two South East Asian (SEA) faces are considered in this work for the emotion classification. There are six emotions and one neutral are considered as the output. Each subject shows unique characteristic of the lip and eye features for various emotions. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters and it had been proven by applying to two SEA subjects and have improved the classification rate.

Keywords: ellipse fitness function, genetic algorithm, emotion recognition, fuzzy clustering

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: Amdouni Hamida, Gammoudi Mohamed Mohsen

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In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process.

Keywords: association rules, rule-based classification, classification quality, validation

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Authors: Johannes Van Der Sandt

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This paper explores and reflects on the power of music, and more specific singing as an instrument for integration, inclusion, group cohesion, collective cooperation, repairing social relationships and facilitating dialogue between groups in conflict. The General Assembly of the United Nations has declared the 21st of September as International Day of Peace. This day is dedicated to advocate and strengthen among all people, an annual day to strive for no violence and cease-fire. What role does music play in strengthening ideals of peace? The findings of this paper is a result of field and online research as well as a literature survey to identify the most important examples of institutions, instruments or initiatives where music serves as a vehicle for the transmission and promoting of peace ideals and acting to assist movements for social change. Important examples where singing and music were used as tools for peace activism are the 1987 Estonian Singing Revolution and the more recent peace engagement in the Afghan Conflict, both very good examples of the cultural capital of the local population used as catalyst for promoting peace. The author offers a concise and relevant overview of such initiatives with the aim to validate the power of music and song as tools to support the United Nation’s Declaration on the Promotion Among Youth of the Ideals of Peace, Mutual Respect and Understanding Between Peoples: Young people should be educated and made aware of the ideals of peace. They should be educated in a spirit of mutual understanding and respect for one another in order to develop an attitude of striving for equal rights for all human beings, believing in economic and social growth for all, together with a belief in disarmament and working towards the maintenance of peace and security worldwide.

Keywords: conflict, music, peace, singing

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Authors: James Waide

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In music, subject typically denotes the initial idea to which the entire composition refers—a concept congruous with Aristotle's notion of subject as primary substance, in the sense of an irreducible this particular. Gioseffo Zarlino, who established subject (soggetto) as a musical term, insisted the composer as “rediscovering” the subject within their music in order to “[bring] it to perfection” and, furthermore, that if the composer had not rediscovered the subject already, then one would simply take the first part of the composition to be the subject. Meanwhile, Žižek reads the Hegelian subject (as negativity set against positive object) through Lacanian Psychoanalysis (in which the subject is a kind of fictive entity of the clinic: a mere appearance which sits atop the objects of analysis) in the concept of Absolute Recoil. For Žižek, subject exists retroactively in Absolute Recoil from object, meaning subject is a void which only has meaning because of the object it is seen through. Following the work of theorists such as Adorno and Althusser, one can understand the ideological construction of such a subject. It may be argued that in Zarlino, musical subject can be similarly read as retroactively constructed, either by the composer or the listener. Furthermore, in recent work, Samuel Wilson identifies different kinds of subjects in music which can be psychoanalytically examined, including the fictive subject: a purely musical entity raised to the level of psychoanalytic subject. On which basis if, as Adorno insisted, 'authentic' music constitutes 'cognition without concepts', where and what is this subject without concept?.

Keywords: absolute recoil, critical theory, ideology, music analysis, psychoanalysis, retroactivity, subject

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: Dustin Dill

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The physical movement of crossing over water points to both developing narrative tropes and innovative cinematography in World Cinema today. Two prime examples, Alamar (2009) by Pedro González-Rubio and Kaili Blues (2015) by Bi Gan, demonstrate how contemporary storytelling in a film not only rests upon these water shots but also emerges from them. The range of symbolism that these episodes in the story provoke goes hand in hand with the diverse filming sequences found in the respective productions. While González-Rubio decides to cut the scene into long and longer shots, Gan uses a single take. The differing angles depict equally unique directors and film projects: Alamar runs parallel to many definitions of the essay film, and Kaili Blues resonates much more with mystery and art film. Nonetheless, the crossing of water scenes influence the narratives’ subjects despite the generic consequences, and it is within the essay, mystery, and art film genres which allows for a better understanding of World Cinema. Tiago de Luca explains World Cinema’s prerogative of giving form to a certain type of spectator does not always line up. Given the immense number of interpretations of crossing water —the escape from suffering to find nirvana, rebirth, and colonization— underline the difficulty of categorizing it. If before this type of cross-genre was a trait that defined World Cinema in its beginning, this study observes that González-Rubio and Gan question the all-encompassing genre with their experimental shots of a universal narrative trope, the crossing of water.

Keywords: cinematography, genre, narrative, world cinema

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving the army, moving convoys etc. The radar operator selects one of the promising targets into single target tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper, we present a technique using mathematical and statistical methods like fast fourier transformation (FFT) and principal component analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, FFT, principal component analysis, eigenvector, octave-notes, DSP

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

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Authors: Karen V. Lee

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The background and significance of this study involves an autoethnographic story that shares research results about how music and movies influence the suicide of a new music teacher working in a public school. The performative narrative duet demonstrates how music and movies highlight social issues when the new teacher cannot cope with allegations surrounding professional issues. Both university advisors are drawn into deep reflection about the wider political issues that arise around the transition from the student-teacher internship process to the teaching career with the stark reality of teaching profession in the 21st century. This performance of story and music creates a transformative composition of reading, hearing, feeling while provoking visceral and emotional responses. Sometimes, young teachers are forced to take a leave of absence to reflect upon their practice with adolescents. In this extreme circumstance, the outcome was suicide. The qualitative research method involves an autoethnographic story as the author is methodologist, theoretician, and participant. Sub-themes surround film, music education and how movie resources have influenced his tragic misguided decision regarding social, emotional, physical, spiritual, and practical strategies to cope with the allegations. Major findings from this story demonstrate how lived experiences can resonate the importance of providing more education and resources to new teachers. The research provides substantive contribution, aesthetic merit, as the impact of movies and music influences the suicide. The reflexive account of storied sensory experiences situated in culture settings becomes a way to describe and seek verisimilitude by evoking lifelike and believable feelings from others. Sadly, the circumstance surrounding the story involving the allegations of a teacher sexually harassing a student is not uncommon in society. However, the young teacher never received counseling to cope with the allegations but instead was influenced by music and movies and opted for suicide. In conclusion, stories share the implications for film and media studies as music and movies can encourage a moral mission to empower individuals with despair and emotional impairment to embrace professional support to assist with emotional and legal challenges encountered in the field of teaching. It is from media studies that education and awareness surrounding suicide can disseminate information about the tragic outcome.

Keywords: music, movies, suicide, narrative, autoethnography

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Authors: Dileep G., Akash Singh, Dalchand Ahirwar, Arkadeep Ghosh, Ashutosh Purohit, Gaurav Guleria, Kshatriya Om Prashant, Pushkar Patel, Saksham Kumar, Vanshaj Nathani, Vikas Dangi, Shubhajit Roy Chowdhury, Varun Dutt

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Transcranial direct current stimulation (tDCS) has shown promise as a novel approach to enhance cognitive performance and provide therapeutic benefits for various brain disorders. However, the exact underlying brain mechanisms are not fully understood. We conducted a study to examine the brain's functional changes when subjected to simultaneous tDCS and music (Indian classical raga). During the study, participants in the experimental group underwent a 20-minute session of tDCS at two mA while listening to music (raga) for a duration of seven days. In contrast, the control group received a sham stimulation for two minutes at two mA over the same seven-day period. The objective was to examine whether repetitive tDCS could lead to the formation of additional functional connections between the medial prefrontal cortex (the stimulated area) and the auditory cortex in comparison to a sham stimulation group. In this study, 26 participants (5 female) underwent pre- and post-intervention scans, where changes were compared after one week of either tDCS or sham stimulation in conjunction with music. The study revealed significant effects of tDCS on functional connectivity between the stimulated area and the auditory cortex. The combination of tDCS applied over the mPFC and music resulted in newly formed connections. Based on our findings, it can be inferred that applying anodal tDCS over the mPFC enhances functional connectivity between the stimulated area and the auditory cortex when compared to the effects observed with sham stimulation.

Keywords: fNIRS, tDCS, neuroplasticity, music

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Mballa Abanda Serge, Henda Gnakate Biba, Romaric Guemno Kuate, Akono Rufine Nicole, Petfiang Sidonie, Bella Sidonie

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The main hypotheses proposed around the definition of the syllable and of music, of the common origin of music and language, should lead the reader to reflect on the cross-cutting questions raised by the debate on the notion of universals in linguistics and musicology. These are objects of controversy, and there lies its interest: the debate raises questions that are at the heart of theories on language. It is an inventive, original and innovative research thesis. The main hypotheses proposed around the definition of the syllable and of music, of the common origin of music and language, should lead the reader to reflect on the cross-cutting questions raised by the debate on the notion of universals in linguistics and musicology. These are objects of controversy, and there lies its interest: the debate raises questions that are at the heart of theories on language. It is an inventive, original and innovative research thesis. A contribution to the theoretical, musicological, ethno musicological and linguistic conceptualization of languages, giving rise to the practice of interlocution between the social and cognitive sciences, the activities of artistic creation and the question of modeling in the human sciences: mathematics, computer science, translation automation and artificial intelligence. When you apply this theory to any text of a folksong of a world-tone language, you do not only piece together the exact melody, rhythm, and harmonies of that song as if you knew it in advance but also the exact speaking of this language. The author believes that the issue of the disappearance of tonal languages and their preservation has been structurally resolved, as well as one of the greatest cultural equations related to the composition and creation of tonal, polytonal and random music. The experimentation confirming the theorization, It designed a semi-digital, semi-analog application which translates the tonal languages of Africa (about 2,100 languages) into blues, jazz, world music, polyphonic music, tonal and anatonal music and deterministic and random music). To test this application, I use a music reading and writing software that allows me to collect the data extracted from my mother tongue, which is already modeled in the musical staves saved in the ethnographic (semiotic) dictionary for automatic translation ( volume 2 of the book). Translation is done (from writing to writing, from writing to speech and from writing to music). Mode of operation: you type a text on your computer, a structured song (chorus-verse), and you command the machine a melody of blues, jazz and world music or variety etc. The software runs, giving you the option to choose harmonies, and then you select your melody.

Keywords: music, entanglement, langauge, science

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Authors: Khadija Bouzaachane, El Mahdi El Guarmah

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The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake.

Keywords: classification, deep learning, reinforcement learning, satellite imagery

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Authors: Rita A. Sunday-Kanu

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This article reports on an evaluation of lunch hour concert and its effectiveness in improving undergraduate performance ability. In particular, it examines the aptitude of students in classroom applied music and their reaction/responses to true life concert situations. It further investigated factors affecting students’ confidence during performances, the relationship between stage fright and confidence building in regular concert participation. The Department of Music, University of Port Harcourt runs monthly lunch our concerts which are coordinated by undergraduates for the university community. Forty music students who have participated in or coordinated lunch hour concerts were chosen for this survey. Eight music lecturers who have supervised the monthly lunch hour concert were also chosen for this study. The attitude and view on the effectiveness of lunch hour concert in enhancing students’ performance skills were gotten through questionnaires survey, in-depth interview and participant observation to determine if classroom based applied music alone is as successful in grooming performance genius as the lunch hour concert. Result indicated that students’ participation in lunch hour concert did indeed broaden and strengthened their performance experiences. This observation led to a recommendation that regular community based concerts be considered as a standard for performance practices in the university curriculum since it serves as a preparatory platform for acquiring professional performance skills before graduation.

Keywords: lunch hour concert, performance, performing skill, community concert

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: María Del Carmen Baena Lupiáñez

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Music has been considered for decades as a tool to express emotions. In the case of people with some physical disabilities, they are able to develop what is known as echolocation. It means that the development of some of the senses to compensate the lack of one of them. For instance, it has been proved that people with reduced visibility have a more developed hearing capacity. In series or films, music is fundamental to contextualize the viewer in the story. Music becomes an indispensable element to make people with reduced visibility to understand the plot and to avoid ambiguities, in the absence of an audio description. Since the songs of Japanese animes have not been as studied as other soundtracks from this point of view to our knowledge, the three openings of the anime Shingeki no Kyojin have been chosen to carry out the experiment from which this study will start. It will test the perceptions of people with reduced visibility by reproducing the three opening songs of Shingeki no Kyojin and asking the users questions related to their thoughts about the anime, their feelings when listening to these songs and the possible story that this anime could be about. In fact, users could identify in general the plot of the story and their perceptions corresponded to what the songs should transmit, taking into account its chords and harmonies.

Keywords: anime, argumental contextualization, echolocations, emotions

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Andrey V. Timofeev

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This paper introduces an original method for guaranteed estimation of the accuracy of an ensemble of Lipschitz classifiers. The solution was obtained as a finite closed set of alternative hypotheses, which contains an object of classification with a probability of not less than the specified value. Thus, the classification is represented by a set of hypothetical classes. In this case, the smaller the cardinality of the discrete set of hypothetical classes is, the higher is the classification accuracy. Experiments have shown that if the cardinality of the classifiers ensemble is increased then the cardinality of this set of hypothetical classes is reduced. The problem of the guaranteed estimation of the accuracy of an ensemble of Lipschitz classifiers is relevant in the multichannel classification of target events in C-OTDR monitoring systems. Results of suggested approach practical usage to accuracy control in C-OTDR monitoring systems are present.

Keywords: Lipschitz classifiers, confidence set, C-OTDR monitoring, classifiers accuracy, classifiers ensemble

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Authors: Natascha Reich

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In colonial Peru, the Spanish crown relied on religious orders, most notably Dominicans, Franciscans, and Jesuits, for accelerating processes of colonization. The dissemination of Christian art, architecture, and music, and most of all, the agency of indigenous people in their production played a key role in facilitating the acceptance of the new religious and political system. Current research on Peruvian colonial music culture and its role as a vehicle for colonization focus on practices in urban centers. The lack of (written) primary sources seems to turn rural areas into a less attractive research territory for musicologists. This paper advocates for a more inclusive approach. By investigating seventeenth-century pipe organs as material remains of Franciscan missionary music culture, it shows how reactions to colonial forces and Christianization in rural Andean locations could follow tendencies different from those in urban areas. Indigenous musicians in cities tried to 'fit' into the European system in order to be accepted by the ruling Spanish elite. By contrast, the indigenous-built pipe organs in the rural Peruvian Colca-Valley show distinctly native-Andean influences. This paper argues that this syncretism can be interpreted as hybridity in Homi K. Bhabha’s sense, as a means of the colonized to undermine the power of the colonizer and to advance reactionary politics. Not only will it show the necessity of considering rural Peruvian music history in modern scholarship for arriving at a more complete picture of colonial culture, but it will also evidence the advantages of a mixed-methodology approach. Historical organology, combined with concepts from ethnomusicology and post-colonial studies, proves as a useful tool in the absence or scarcity of written primary sources.

Keywords: cultural hybridity, music as reactionary politics, Latin American pipe organs, Peruvian colonial music

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Authors: Nawras Kurzom, Avi Mendelsohn

Abstract:

The effects of background music on learning and memory are inconsistent, partly due to the intrinsic complexity and variety of music and partly to individual differences in music perception and preference. A prominent musical feature that is known to elicit strong emotional responses is musical tension. Musical tension can be brought about by building anticipation of rhythm, harmony, melody, and dynamics. Delaying the resolution of dominant-to-tonic chord progressions, as well as using dissonant harmonics, can elicit feelings of tension, which can, in turn, affect memory formation of concomitant information. The aim of the presented studies was to explore how forming declarative memory is influenced by musical tension, brought about within continuous music as well as in the form of isolated chords with varying degrees of dissonance/consonance. The effects of musical tension on long-term memory of declarative information were studied in two ways: 1) by evoking tension within continuous music pieces by delaying the release of harmonic progressions from dominant to tonic chords, and 2) by using isolated single complex chords with various degrees of dissonance/roughness. Musical tension was validated through subjective reports of tension, as well as physiological measurements of skin conductance response (SCR) and pupil dilation responses to the chords. In addition, music information retrieval (MIR) was used to quantify musical properties associated with tension and its release. Each experiment included an encoding phase, wherein individuals studied stimuli (words or images) with different musical conditions. Memory for the studied stimuli was tested 24 hours later via recognition tasks. In three separate experiments, we found positive relationships between tension perception and physiological measurements of SCR and pupil dilation. As for memory performance, we found that background music, in general, led to superior memory performance as compared to silence. We detected a trade-off effect between tension perception and memory, such that individuals who perceived musical tension as such displayed reduced memory performance for images encoded during musical tension, whereas tense music benefited memory for those who were less sensitive to the perception of musical tension. Musical tension exerts complex interactions with perception, emotional responses, and cognitive performance on individuals with and without musical training. Delineating the conditions and mechanisms that underlie the interactions between musical tension and memory can benefit our understanding of musical perception at large and the diverse effects that music has on ongoing processing of declarative information.

Keywords: musical tension, declarative memory, learning and memory, musical perception

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Authors: Joni Asitashvili, Eka Chabashvili, Maya Virsaladze, Alexander Chokhonelidze

Abstract:

Studying the soundscape is a major challenge in many countries of the civilized world today. The sound environment and music itself are part of the Earth's ecosystem. Therefore, researching its positive or negative impact is important for a clean and healthy environment. The acoustics of nature gave people many musical ideas, and people enriched musical features and performance skills with the ability to imitate the surrounding sound. For example, a population surrounded by mountains invented the technique of antiphonal singing, which mimics the effect of an echo. Canadian composer Raymond Murray Schafer viewed the world as a kind of musical instrument with ever-renewing tuning. He coined the term "Soundscape" as a name of a natural environmental sound, including the sound field of the Earth. It can be said that from which the “music of nature” is constructed. In the 21st century, a new field–Ecomusicology–has emerged in the field of musical art to study the sound ecosystem and various issues related to it. Ecomusicology considers the interconnections between music, culture, and nature–According to the Aaron Allen. Eco-music is a field of ecomusicology concerning with the depiction and realization of practical processes using modern composition techniques. Finding an artificial sound source (instrumental or electronic) for the piece that will blend into the soundscape of Sound Oases. Creating a composition, which sounds in harmony with the vibrations of human, nature, environment, and micro- macrocosm as a whole; Currently, we are exploring the ambient sound of the Georgian urban and suburban environment to discover “Sound Oases" and compose Eco-music works. We called “Sound Oases" an environment with a specific sound of the ecosystem to use in the musical piece as an instrument. The most interesting examples of Eco-music are the round dances, which were already created in the BC era. In round dances people would feel the united energy. This urge to get united revealed itself in our age too, manifesting itself in a variety of social media. The virtual world, however, is not enough for a healthy interaction; we created plan of “contemporary round dance” in sound oasis, found during expedition in Georgian caves, where people interacted with cave's soundscape and eco-music, they feel each other sharing energy and listen to earth sound. This project could be considered a contemporary round dance, a long improvisation, particular type of art therapy, where everyone can participate in an artistic process. We would like to present research result of our eco-music experimental performance.

Keywords: eco-music, environment, sound, oasis

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