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Soft Computing
Published in: Soft Computing 13/2020

12-11-2019 | Methodologies and Application

Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition

Authors: Ralph Abboud, Joe Tekli

Published in: Soft Computing | Issue 13/2020

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Abstract

Over the past years, several approaches have been developed to create algorithmic music composers. Most existing solutions focus on composing music that appears theoretically correct or interesting to the listener. However, few methods have targeted sentiment-based music composition: generating music that expresses human emotions. The few existing methods are restricted in the spectrum of emotions they can express (usually to two dimensions: valence and arousal) as well as the level of sophistication of the music they compose (usually monophonic, following translation-based, predefined templates or heuristic textures). In this paper, we introduce a new algorithmic framework for autonomous music sentiment-based expression and composition, titled MUSEC, that perceives an extensible set of six primary human emotions (e.g., anger, fear, joy, love, sadness, and surprise) expressed by a MIDI musical file and then composes (creates) new polyphonic (pseudo) thematic, and diversified musical pieces that express these emotions. Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. We conducted a large battery of tests to evaluate MUSEC’s effectiveness and efficiency in both sentiment analysis and composition. It was trained on a specially constructed set of 120 MIDI pieces, including 70 sentiment-annotated pieces: the first significant dataset of sentiment-labeled MIDI music made available online as a benchmark for future research in this area. Results are encouraging and highlight the potential of our approach in different application domains, ranging over music information retrieval, music composition, assistive music therapy, and emotional intelligence.
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Appendix
Available only for authorised users
Footnotes
1
Simplest form of musical textures where only one note is played at a time, in contrast with polyphonic music where more than one note is played simultaneously.
 
2
Musical Instrument Digital Interface: a digital music format designed for symbolic music representation and processing by computers.
 
3
A fuzzy classifier is a classifier which assigns membership scores to input data objects, producing fuzzy categories with fuzzy boundaries, such that an object, e.g., a musical piece, can be part of one category and the other at the same time (e.g., 80% excitement and 20% fear), in contract with traditional crisp classifiers which categorize data in crisp/distinct categories (Kotsiantis 2007). In our current system, we utilize fuzzy k-NN due to its flexibility and effectiveness, yet any other fuzzy classifier could be used, e.g., (Abu 2017; Abu et al. 2016; Amin et al. 2018; Fahmi et al. 2017, 2018, 2019).
 
4
A chord is a combination of 3 or more notes (cf. Sect. 2).
 
6
Available online at: http://​sigappfr.​acm.​org/​Projects/​MUSEC, including MUSEC synthetic compositions and all experimental results.
 
7
A fundamental frequency is the lowest frequency produced by the oscillation of an object. In music, it is perceived as the lowest partial (simple tone) present that is distinct from the harmonics of higher frequency. In the remainder of this paper, terms frequency and fundamental frequency will be used interchangeably, unless explicitly stated otherwise.
 
8
Music produced following the traditions of Western (European) culture, compared with Oriental (Byzantine, Mizrahi, or Asian) music.
 
9
Also referred to in the literature as affective music composition.
 
10
With respect to.
 
11
Also known as.
 
12
Support vector machine.
 
13
k-nearest neighbor.
 
14
An ANN with several hidden layers between the input and the output layers is called a deep neural network or a deep learner.
 
15
It is a machine learning approach which allows the learning of a function that maps an input (e.g., musical piece) to an output (e.g., sentiment category or sentiment score) based on sample input–output pairs, so-called labeled training data, where each sample pair consists of a given input object (e.g., a music feature vector) and a desired output value (e.g., a sentiment category or a sentiment score). The produced mapping function is an approximation of the true mapping function between the sample training pairs (Kotsiantis 2007).
 
16
An evolutionary algorithm can be defined as a population-based metaheuristic optimization algorithm, which uses mechanisms inspired by biological evolution, such as reproduction, mutation, crossover, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. The evolution of the population then takes place after the repeated application of the above operators (Goldberg 1989; Whitley and Sutton 2012).
 
17
More features could be later added following the user’s needs.
 
18
To determine the dominant key, a chroma histogram for the input music file is first computed, denoting the percentage of total piece duration in which every chroma can be heard. The histogram is later used to compute likelihood scores using Temperley’s key profiles (Temperley 2002). A Bayesian Approach. The key with the highest score is finally selected as the dominant key (Temperley 2002).
 
19
Dominant key misidentification can occasionally occur, particularly for pieces where modulations occur very frequently and for atonal music (Temperley 2002; Kyogu 2008) (e.g., modern music which does not abide by a fixed key).
 
20
Note that 100% accuracy in chord progression identification is difficult to obtain due to the very nature of chord progressions: where i) the same chord progression can be played in so many different ways while still portraying the same musical structure, and ii) it can be often difficult to separate between consecutive chords since notes are sometimes combined between them. Our heuristic performs accurately on relatively simple music where there is a clear chord structure, and a clear separation between chords with no rapid transitions between them.
 
21
It requires O(n × m log(n + m)) where n and m designate the number of chords in the two pieces (chord progressions sequences) being processed.
 
22
Consider two chord progression sequences A and B, consisting of chords A1, A2, …, Am and B1, B2, …, Bn, respectively. Without loss of generality, consider the case where m < n. Following the standard TPSD algorithm in Ayadi et al. (2016), the shorter sequence is compared with the longer one at every position, e.g., A1, …, Am versus B1,…,Bm, then A1, …, Am versus B2,…,Bm+ 1, and so forth until A1, …, Am versus Bn−m,…,Bn. Then the comparison yielding the smallest difference is selected as the final similarity (or distance) value. With the more efficient version of the TPSD algorithm in Bas De Haas et al. (2013), the chord progression sequences are only compared from their starting positions, e.g., A1, …, Am is only compared with B1,…,Bm, and that score is utilized as the chord progressions similarity (distance) score. Despite this linear relaxation of the original algorithm, TPSD computation remains the most expensive among all other feature similarity computations put together (cf. experiments in Sect. 6.2.2).
 
23
To the expense of a potential loss of precision when processing long musical pieces (consisting of a large chord progression sequences).
 
24
Available online at: http://​sigappfr.​acm.​org/​Projects/​MUSEC, SL survey form #1 (first part, 24-pieces), #2 (second part, 8-pieces), and #3 (third part, 8-pieces), along with the resulting sentiment-labeled dataset.
 
25
In our current implementation of MC, we hard-coded the chord probability distribution (through which a chord is selected) based on empirical sampling from our training set. Yet, learning the chord probability distribution can be a research project in and of itself, and can entail different composition styles. For instance, the distribution could be learned from a composer’s composition corpus, to produce pieces following the composer’s own style (which we further discuss as an ongoing work in Sect. 8).
 
26
Randomness is guided by MUSEC’s KB music-theoretic rules.
 
27
Emphasizing sentiment expression, while also promoting diversity.
 
28
Pearson correlation coefficient. Note that any other vector similarity measure (such as cosine or dice) could have been used. We adopt PCC here since it is commonly utilized in the literature (Abbasi et al. 2008; O’Connor et al. 2010).
 
29
We consider this strategy to be similar to the way some human composers usually write music: producing multiple candidate (trial) pieces, slicing and mixing them up, developing them and making them evolve until reaching a final pool of best candidates, from which the single best candidate is usually adopted as the actual final piece.
 
30
We adopted a ratio R = 0.7 in our current study, so that 70% of the offspring would be subject to fitness trimming, whereas only 30% would undergo variability trimming.
 
32
Note that the number of beats in a piece is naturally less than the number of notes. While there is no straightforward relationship between the two, they can be paralleled to sentences and words in flat text: where beats represent music sentences, and notes represent the sentences’ words. In our sample test dataset of 100 pieces, the number of beats was on average 4-to-8 times less than the number of notes.
 
33
PCC = δXY/(δX × δY) where: x and y designate user and system generated similarity values, respectively, δX and δY denote the standard deviations of x and y, respectively, and δXY denotes the covariance between the x and y variables. The values of PCC ∈ [− 1, 1] such that: − 1 designates that one of the variables is a decreasing function of the other variable (i.e., music pieces deemed similar by human testers are deemed dissimilar by the system, and vice versa), 1 designates that one of the variables is an increasing function of the other variable (i.e., pieces are deemed similar/dissimilar by human testers and the system alike), and 0 means that the variables are not correlated.
 
34
MSE, computed as an average Euclidian distance measure, is a good indication of how close similarity scores are to human ratings: one by one (for every pair of pieces), whereas PCC compares the behavior of the vector of similarity ratings (for all pairs or pieces) as a whole.
 
36
http://​sigappfr.​acm.​org/​Projects/​MUSEC, SL survey form #1 (first part, 24-pieces), #2 (second part, 8-pieces), and #3 (third part, 8-pieces).
 
37
While we could have asked the testers to provide a confidence score associated with every sentiment score, yet, we felt this would complicate things for non-expert testers, especially that our objective was to capture their inherent feelings when listening to the music pieces, rather than have them “rationalize” their ratings by adding confidence scores. Nonetheless, considering tester rating confidence is an interesting factor that we plan to evaluate in a future study.
 
38
With the 100-piece training set, the system had “less” to learn since it was training on a more or less homogeneous training set, and thus over-fitted w.r.t. the well represented sentiments, namely joy and sadness, but was less successful in inferring less represented sentiments like anger and fear.
 
39
To help illustrate this concept, let’s consider the following example, consisting of three vectors: V1 = (0.8, 0.6), V2 = (0.95, 0.45), and V3 = (0.65, 0.75). Let V1 be our target vector and let V2 and V3 be our system estimate vectors. Upon first inspection, it is obvious that V2 is a better representative of V1 than V3, since it more or less exhibits the same behavior as V1 (higher first term). This similarity in behavior is visible through PCC, where PCC(V1, V2) = 1 and PCC(V1, V3) = − 1. However with MSE, we obtain MSE(V1,V2) = MSE(V1,V3) = 0.0225. This shows that MSE is only a good indication of how close scores are to target sentiments one by one, while PCC reflects the overall similarity of a predicted sentiment vector to the target vector as a whole.
 
40
The Turing test was proposed by Alan Turing in 1950, designed to test the ability of a machine to exhibit intelligent behavior that is equivalent to or indistinguishable from that of a human. It was originally used to evaluate machines mimicking human conversation (originally referred to as the “imitation game”). A machine passes the Turing test if, after a number of questions, the human tester (asking questions) cannot know if the answers come from a human or a machine (Epstein et al. 2009).
 
41
Anthony Bou Fayad is a processional composer, pianist, and music instructor in the Antonine University’s School of Music, located in Baabda, Mont Lebanon. He also holds a Master’s of Computer Engineering, specializing in multimedia data processing, which allowed him to easily understand the context and purpose of our study, helping us set up the experimental process. Mr. Bou Fayad was partly remunerated for his efforts, mainly for playing and digitally recording all pieces, while volunteering his consulting services.
 
42
Some music composition systems provide sample pieces online, e.g., (Diaz-Jerez 2011; SACEM 2016), yet none of them are sentiment-based.
 
43
Using a population size S = 50, a generation size N varying between 50 and 80, a branching factor B = 10 and a fitness-to-variability ratio R = 0.7. All mutation probabilities were set to 0.1.
 
44
Available online at: http://​sigappfr.​acm.​org/​Projects/​MUSEC, MC survey forms #1-to-#10.
 
47
Recall that states where both valence and arousal dimensions converge (e.g., both valence and arousal are high, or both are low) occur more often than states were they diverge, indicating a potential bias or ambiguity in the model (as stated by the model’s creator in (Russell 1980)).
 
49
http://​www.​lau.​edu.​lb/​news-events/​news/​archive/​music_​composers_​face_​off_​with_​/​. The event included an active participation from a live audience of LAU students, faculty, staff, and friends, who helped rate MUSEC’s compositions and evaluate its sentiment scoring accuracy.
 
Literature
Abbasi A, Chen H, Thoms S, Fu T (2008) Affect analysis of web forums and blogs using correlation ensembles. IEEE Trans Knowl Data Eng 20(9):1168–1180
Abboud R, Tekli J (2018) MUSE prototype for music sentiment expression. In: IEEE international conference on cognitive computing (ICCC'18). San Francisco, pp 106–109
Abu AO (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl 28(7):1591–1610
Abu AO, Abo-Hammour ZS (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415MathSciNetMATH
Abu AO, Al-Smadi M, Momani S, Hayat T (2016) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20(8):3283–3302MATH
Adiloglu K, Alpaslan FN (2007) A Machine Learning Approach to Two-Voice Counterpoint Composition. Knowl-Based Syst 20(3):300–309
Amin F, Fahmi A, Abdullah S, Ali A, Ahmed R, Ghani F (2018) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. J Intell Fuzzy Syst 34(4):2401–2416
Ayadi MG, Bouslimi R, Akaichi J (2016) A medical image retrieval scheme with relevance feedback through a medical social network. Soc Netw Anal Min 6(1):53:1–53:23
Baeza-Yates R, Ribeiro-Neto B (2011) Modern information retrieval: the concepts and technology behind search, 2nd edn. ACM Press Books, New York
Barrett FS, Grimm KJ, Robins RW, Wildschut T, Sedikides C (2010) Music-evoked nostalgia: affect, memory, and personality. Emotion 10(3):390–403
Bas De Haas W, Veltkamp RC, Wiering F (2008) Tonal pitch step distance: a similarity measure for chord progressions. In: International society of music information retrieval (ISMIR), pp 51–56
Bas De Haas W, Wiering F, Veltkamp RC (2013) A geometrical distance measure for determining the similarity of musical harmony. Int J Multimed Inf Retr 2(3):189–202
Berrett LF (2017) How emotions are made: the secret life of the brain. Macmillan, London
Boden MA (1994) Precis of the creative mind: myths and mechanisms. Behav Brain Sci 17(3):519–570MathSciNet
Bradley M, Lang P (1999) Affective norms for English Words (ANEW): instruction manual and affective ratings. Technical report C-1, Center for Research in Psychophysiology, University of Florida
Burton AR (1998) A hybrid neuro-genetic pattern evolution system applied to musical composition. Ph.D. thesis, University of Surrey, UK
Cai Z, Hu H (2018) Session-aware music recommendation via a generative model approach. Soft Comput 22(3):1023–1031MATH
Cao Y, Jia L, Chen Y, Lin N, Yang C, Zhang B, Liu Z, Li X, Dai H (2019) Recent advances of generative adversarial networks in computer vision. IEEE Access 7:14985–15006
Carnie A (2013) Syntax: a generative introduction, 3rd edn. Wiley, Malden
Chen Y, Garcia E, Gupta M, Rahimi A, Cazzanti L (2009) Similarity-based classification: concepts and algorithms. J Mach Learn Res 10:747–776MathSciNetMATH
Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. MIT Press, CambridgeMATH
Costa Y, Oliveira L, Silla C Jr (2017) An evaluation of convolutional neural networks for music classification using spectrograms. Appl Soft Comput 52:28–38
Danhauser A (1994) Theory of music (French). Henri Lemoine, Paris (original edition published in 1950)
Dell AG, Newton DA, Petroff JG (2011) Assistive technology in the classroom: enhancing the school experiences of students with disabilities, 2nd edn. Pearson, New Delhi
Demopoulos RJ, Katchabaw MJ (2007) Music information retrieval: a survey of issues and approaches. Technical report #677, Department of Computer Science, University of Western Ontario
Diaz-Jerez G (2011) Composing with melomics: delving into the computational world for musical inspiration. MIT Press J 21:3–14
Dubois RL (2003) Applications of generative string-substitution systems in computer music. Ph.D. dissertation, Columbia University
Ekman P (1993) Facial expression of emotion. Am Psychol 48:384–392
Epstein R, Roberts G, Beber G (2009) Parsing the Turing test: philosophical and methodological issues in the quest for the thinking computer, 2009th edn. Springer, BerlinMATH
Fahmi A, Abdullah S, Amin F, Siddiqui N, Ali A (2017) Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems. J Intell Fuzzy Syst 33(6):3323–3337
Fahmi A, Abdullah S, Amin F, Ali A, Khan WA (2018) Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making. J Intell Fuzzy Syst 35(2):2485–2499
Fahmi A, Abdullah S, Amin F, Sajjad Ali Khan M (2019) Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making. Soft Comput 24(14):5753–5783MATH
Fernandez J, Vico F (2013) AI methods in algorithmic composition: a comprehensive survey. J Artif Intell Res 48:513–582MathSciNet
Fleischman MB, Deb KR (2013) Displaying estimated social interest in time-based media. U.S. patent no. 8,516,374
Ghosh A, Strehl J (2003) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583–617MathSciNetMATH
Gkonou C, Mercer S (2017) Understanding emotional and social intelligence among English language teachers. ELT research papers 17.03, British Council, ISBN 978-0-86355-842-9
Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingMATH
Goleman D (2005) Emotional intelligence: why it can matter more than IQ, Bantam Books 10th Anniversary edn. Bloomsbury Publishing, London
Hauger D, Schedl M, Kosir A, Tkalcic M (2013) The million musical tweet dataset: what we can learn from microblogs. In: Proceedings of the 14th international society for music information retrieval conference (ISMIR’13)
Hevner K (1935) The affective character of the major and minor modes in music. Am J Psychol 47(1):03–118
Hiller L (1970) Music composed with computers: a historical survey. In: Lincoln HB (ed) The computer and music. Cornell University Press, Ithaca, pp 42–97
Hiller L, Isaaccson L (1959) Experimental music: composition with an electronic computer. McGraw-Hill, New York
Hoeberechts M, Shantz J (2009) Realtime emotional adaptation in automated composition. In: Proceedings of audio mostly, pp 1–8
Hopfield J, Tank D (1985) Neural computation of decisions in optimization problems. Biol Cybern 52(3):52–141MATH
Hovy E (2015) What are sentiment, affect, and emotion? Applying the methodology of Michael Zock to sentiment analysis. In: Gala N et al (eds) Language production, cognition, and the lexicon, text, speech and language technology, vol 48. Springer, Berlin, pp 13–24
Huang C, Lin E (2013) An emotion-based method to perform algorithmic composition. In: The 3rd international conference on music & emotion, pp. 244–247
Husarik S (1983) John Cage and LeJaren Hiller: HPSCHD, 1969. Am Music 1(2):1–21
Iakovidou C, Anagnostopoulos N, Kapoutsis A, Chatzichristofis Y, Boutalis Y (2014) Searching images with MPEG-7 (& MPEG-7-like) powered localized descriptors: the SIMPLE answer to effective content based image retrieval. In: 12th international workshop on content-based multimedia indexing (CBMI). pp 18–20
Iren D, Liem C, Yang J, Bozzon A (2016) Using social media to reveal social and collective perspectives on music. In: International ACM conference on web sciencs (WebSci’16), Hannover, Germany, pp 296–300
Katayose H, Kato H, Imai M, Inokuchi S (1989) An approach to an artificial music expert. In: International computer music conference, pp 138–146
Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 4:580–585
Kim J, Wigram T, Gold C (2009) Emotional, motivational and interpersonal responsiveness of children with autism in improvisational music therapy. Autism 13(4):389–409
Kirke A, Miranda ER (2009) A survey of computer systems for expressive music performance. ACM Comput Surv (CSUR) 42(1):3
Kirke A, Miranda E (2011) Combining EEG frontal asymmetry studies with affective algortihmic composition and expressive performance model. In: International computer music conference, Huddersfield
Kirke A, Miranda E (2017) Aiding soundtrack composer creativity through automated film script-profiled algorithmic composition. J Creat Music Syst 1(2)
Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268MathSciNetMATH
Kyogu L (2008) A system for acoustic chord transcription and key extraction from audio using hidden Markov models trained on synthesized audio. Dissertation, Department of Music, Stanford University
L’Hadj LS, Boughanem M, Amrouche K (2016) Enhancing information retrieval through concept-based language modeling and semantic smoothing. J Assoc Inf Sci Technol (JASIST) 67(12):2909–2927
Lin CL, Shih YH, Tzeng GH, Yu HC (2016) A service selection model for digital music service platforms using a hybrid MCDM approach. Appl Soft Comput 48:385–430
Liu J, Zhong W, Jiao L (2010) A multiagent evolutionary algorithm for combinatorial optimization problems. IEEE Trans Syst Man Cybern 40(1):229–240
Livingstone SR et al (2010) Changing musical emotion: a computational rule system for modifying score and performance. Comput Music J 34(1):41–64
Manousakis S (2006) Musical L-systems. Koninklijk Conservatorium, The Hague Master Thesis
Marques M, Oliveira V, Vieira S, Rosa AC (2000) Music composition using genetic evolutionary algorithms. In: Proceedings of the IEEE conference on evolutionary computation. IEEE Press, New York, NY
McChord KA (2004) Moving beyond “that’s all i can do”: encouraging musical creativity in children with learning disabilities. Bull Counsil Res Music Educ 159:23–32
McCormack J (1996) Grammar-based music composition. In: Stocker S et al (eds) Complex systems. IOS Press, Amsterdam, pp 321–336
Molina A, Daniel D, Moya JC, Vico FJ (2016) An Evo-Devo system for algorithmic composition that actually works. In: Proceedings of the 2016 on genetic and evolutionary computation conference companion. ACM, pp 37–38
Morreale F, de Angeli A (2016) Collaborating with an autonomous agent to generate affective music. ACM Trans Comput Entertain (ACM CIE) 14(3):1–21
Mühling M et al (2016) Content-based video retrieval in historical collections of the German broadcasting archive. In: International conference on theory and practice of digital libraries (TPLD’16), pp 67–78
O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the fourth international AAAI conference on weblogs and social media, pp 122–129
Orio N (2006) Music retrieval: a tutorial and review. Found Trends Inf Retr 1(11):90MATH
Ozcan E, Erçal T (2008) A genetic algorithm for generating improvized music. Lecture notes in computer science. Springer, Heidelberg, p 4926
Panda R, Malheiro R, Rocha B, Oliveira A, Paiva RP (2013) Multi-modal music emotion recognition: a new dataset, methodology and comparative analysis. In: 10th international symposium on computer music multidisciplinary research (CMMR), pp 1–13
Papadopoulos G, Wiggins G (1999) AI methods for algorithmic composition: a survey, a critical view and future prospects. In: AISB symposium on musical creativity, pp 110–117
Pavlov S, Olsson C, Svensson C, Anderling V, Wikner J, Andreasson O (2014) Generation of music through genetic algorithms. Bachelor’s Thesis, University of Gothenburg, Sweden
Prusinkiewicz P, Lindenmayer A (1990) The algorithmic beauty of plants. Springer, New YorkMATH
Rahim A, Civelek I, Liang FH (2015) A model of department chairs’ social intelligence & faculty members’ turnover intention. Intelligence 53:65–71
Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst 89:14–46
Reimer MA, Garnett GE (2014) A hierarchical system for autonomous musical creation. In: Tenth artificial intelligence and interactive digital entertainment conference, pp 45–49
Russell J (1980) A circumplex model of affect. J Pers Soc Psychol 6(39):1161–1980
Sandred O, Laurson M, Kuuskankare M (2009) Revisiting the Illiac suite—a rule-based approach to stochastic processes. Sonic Ideas/Ideas Sonicas 2:42–46
Schank RC, Cleary C (1995) Making machines creative. In: Smith S, Ward TB, Finke RA (eds) The creative cognition approach. MIT Press, Cambridge, pp 229–247
Schedl M, Flexer A, Urbano J (2013) The neglected user in music information retrieval research. J Intell Inf Syst (JIIS) 41(3):523–539
Schedl M, Gómez E, Urbano J (2014) Music information retrieval: recent developments and applications. Found Trends Inf Retr 8(2–3):127–161
See CM (2012) The use of music and movement therapy to modify behaviour of children with autism. Pertanika J Soc Sci Hum 20(4):1103–1116
Serra MH (1993) Stochastic composition and stochastic timbre: Gendy3 by Iannis Xenakis. Perspect New Music 237–257
Shang W et al (2005) An improved kNN algorithm—fuzzy kNN. In: Computational intelligence and security, pp 741–746
Song Y, Dixon S, Pearce M (2012) A survey of music recommendation systems and future perspectives. In: 9th international symposium on computer music modeling and retrieval, pp 395–410
Subasic P, Huettner A (2001) Affect analysis of text using fuzzy semantic typing. IEEE Trans Fuzzy Syst 9(4):483–496
Temperley D (2002) A Bayesian approach to key-finding. In: International conference on music and artificial intelligence, LNAI 2445, pp 195–206
Troiano L, Birtolo C, Armenise R (2017) Modeling and predicting the user next input by Bayesian reasoning. Soft Comput 21(6):1583–1600
Verbeurgt K, Fayer M, Dinolfo M (2004) A hybrid neural-markov approach for learning to compose music by example. In: Conference of the Canadian society for computational studies of intelligence, pp 480–484
Whipple J (2004) Music in intervention for children and adolescents with autism: a meta-analysis. J Music Ther 41(2):90–106
Whitley D, Sutton AM (2012) Genetic algorithms—a survey of models and methods. In: Rozenberg G, Bäck T, Kok JN (eds) Handbook of natural computing. Springer, Berlin, pp 637–671
Wohlfahrt-Laymanna J, Heimbürgerb A (2017) Content aware music analysis with multi-dimensional similarity measure. Inf Model Knowl Bases XXVIII:292–303
Worth P, Stepney S (2005) Growing music: musical interpretations of L-systems. In: Workshop on applications of evolutionary computing, pp 545–550
Xiao H, Downie SJ (2010) Improving mood classification in music digital libraries by combining lyrics and audio. In: Proceedings of the 10th annual joint conference on digital libraries. ACM, pp 159–168
Yiu R (2013) A composer’s imagining of musical tradition and the reinvention of heritage. Doctoral thesis, City, University of London Institutional Repository
Zangerle E, Pichl M, Gassler W, Specht G (2014) #nowplaying music dataset: extracting listening behavior from Twitter. In: Proceedings of the first international workshop on internet-scale multimedia management (WISMM), ACM Multimedia, Orlando, Florida, USA
Zentner M, Eerola T (2010) Self-report measures and models. In: Juslin PN, Sloboda JA (eds) Handbook of music and emotion: theory, research, applications. Oxford University Press, New York, pp 187–221
Zenz V (2007) Automatic chord detection in polyphonic audio data. Master’s thesis University of Wien, Austria
Metadata
Title
Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition
Authors
Ralph Abboud
Joe Tekli
Publication date
12-11-2019
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 13/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-04503-4

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