6,023 research outputs found
On the Modeling of Musical Solos as Complex Networks
Notes in a musical piece are building blocks employed in non-random ways to
create melodies. It is the "interaction" among a limited amount of notes that
allows constructing the variety of musical compositions that have been written
in centuries and within different cultures. Networks are a modeling tool that
is commonly employed to represent a set of entities interacting in some way.
Thus, notes composing a melody can be seen as nodes of a network that are
connected whenever these are played in sequence. The outcome of such a process
results in a directed graph. By using complex network theory, some main metrics
of musical graphs can be measured, which characterize the related musical
pieces. In this paper, we define a framework to represent melodies as networks.
Then, we provide an analysis on a set of guitar solos performed by main
musicians. Results of this study indicate that the presented model can have an
impact on audio and multimedia applications such as music classification,
identification, e-learning, automatic music generation, multimedia
entertainment.Comment: to appear in Information Science, Elsevier. Please cite the paper
including such information. arXiv admin note: text overlap with
arXiv:1603.0497
On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres
This paper focuses on the modeling of musical melodies as networks. Notes of
a melody can be treated as nodes of a network. Connections are created whenever
notes are played in sequence. We analyze some main tracks coming from different
music genres, with melodies played using different musical instruments. We find
out that the considered networks are, in general, scale free networks and
exhibit the small world property. We measure the main metrics and assess
whether these networks can be considered as formed by sub-communities. Outcomes
confirm that peculiar features of the tracks can be extracted from this
analysis methodology. This approach can have an impact in several multimedia
applications such as music didactics, multimedia entertainment, and digital
music generation.Comment: accepted to Multimedia Tools and Applications, Springe
Problems and opportunities of applying data-& audio-mining techniques to ethnic music
[TODO] Add abstract here
Clustering of Musical Pieces through Complex Networks: an Assessment over Guitar Solos
Musical pieces can be modeled as complex networks. This fosters innovative
ways to categorize music, paving the way towards novel applications in
multimedia domains, such as music didactics, multimedia entertainment and
digital music generation. Clustering these networks through their main metrics
allows grouping similar musical tracks. To show the viability of the approach,
we provide results on a dataset of guitar solos.Comment: to appear in IEEE Multimedia magazin
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A computational study on outliers in world music
The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
Learning a feature space for similarity in world music
In this study we investigate computational methods for assessing music similarity in world music styles. We use state-of-the-art audio features to describe musical content in world music recordings. Our music collection is a subset of the Smithsonian Folkways Recordings with audio examples from 31 countries from around the world. Using supervised and unsupervised dimensionality reduction techniques we learn feature representations for music similarity. We evaluate how well music styles separate in this learned space with a classification experiment. We obtained moderate performance classifying the recordings by country. Analysis of misclassifications revealed cases of geographical or cultural proximity. We further evaluate the learned space by detecting outliers, i.e. identifying recordings that stand out in the collection. We use a data mining technique based on Mahalanobis distances to detect outliers and perform a listening experiment in the ‘odd one out’ style to evaluate our findings. We are able to detect, amongst others, recordings of non-musical content as outliers as well as music with distinct timbral and harmonic content. The listening experiment reveals moderate agreement between subjects’ ratings and our outlier estimation
Methodological contributions by means of machine learning methods for automatic music generation and classification
189 p.Ikerketa lan honetan bi gai nagusi landu dira: musikaren sorkuntza automatikoa eta sailkapena. Musikaren sorkuntzarako bertso doinuen corpus bat hartu da abiapuntu moduan doinu ulergarri berriak sortzeko gai den metodo bat sortzeko. Doinuei ulergarritasuna hauen barnean dauden errepikapen egiturek ematen dietela suposatu da, eta metodoaren hiru bertsio nagusi aurkeztu dira, bakoitzean errepikapen horien definizio ezberdin bat erabiliz.Musikaren sailkapen automatikoan hiru ataza garatu dira: generoen sailkapena, familia melodikoen taldekatzea eta konposatzaileen identifikazioa. Musikaren errepresentazio ezberdinak erabili dira ataza bakoitzerako, eta ikasketa automatikoko hainbat teknika ere probatu dira, emaitzarik hoberenak zeinek ematen dituen aztertzeko.Gainbegiratutako sailkapenaren alorrean ere binakako sailkapenaren gainean lana egin da, aurretik existitzen zen metodo bat optimizatuz. Hainbat datu baseren gainean probatu da garatutako teknika, baita konposatzaile klasikoen piezen ezaugarriez osatutako datu base batean ere
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