A Differential Evolution Algorithm Assisted by ANFIS for Music Fingering

Music fingering is a cognitive process whose goal is to map each note of a music score to a fingering on some instrument. A fingering specifies the fingers of the hands that the player should use to play the notes. This problem arises for many instruments and it can be quite different from instrument to instrument; guitar fingering, for example, is different from piano fingering. Previous work focuses on specific instruments, in particular the guitar, and evolutionary algorithms have been used. In this paper, we propose a differential evolution (DE) algorithm designed for general music fingering (any kind of music instruments). The algorithm uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) engine that learns the fingering from music already fingered. The algorithm follows the basic DE strategy but exploits also some customizations specific to the fingering problem. We have implemented the DE algorithm in Java and we have used the ANFIS network in Matlab. The two systems communicate by using the MatlabControl library. Several tests have been performed to evaluate its efficacy

Splicing Systems from Past to Future: Old and New Challenges

A splicing system is a formal model of a recombinant behaviour of sets of double stranded DNA molecules when acted on by restriction enzymes and ligase. In this survey we will concentrate on a specific behaviour of a type of splicing systems, introduced by P\u{a}un and subsequently developed by many researchers in both linear and circular case of splicing definition. In particular, we will present recent results on this topic and how they stimulate new challenging investigations.Comment: Appeared in: Discrete Mathematics and Computer Science. Papers in Memoriam Alexandru Mateescu (1952-2005). The Publishing House of the Romanian Academy, 2014. arXiv admin note: text overlap with arXiv:1112.4897 by other author

Analysis of touch gestures for online child protection

AbstractThe growth of Internet and the pervasiveness of ICT have led to a radical change in social relationships. One of the drawbacks of this change is the exposure of individuals to threats during online activities. In this context, thetechno-regulationparadigm is inspiring new ways to safeguard legally interests by means of tools allowing to hamper breaches of law. In this paper, we focus on the exposure of individuals to specific online threats when interacting with smartphones. We propose a novel techno-regulatory approach exploiting machine learning techniques to provide safeguards against threats online. Specifically, we study a set of touch-based gestures to distinguish between underages or adults who is accessing a smartphone, and so to guarantee protection. To evaluate the proposed approach's effectiveness, we developed an Android app to build a dataset consisting of more than 9000 touch-gestures from 147 participants. We experimented bothsingle-viewandmulti-viewlearning techniques to find the best combination of touch-gestures able of distinguishing between adults and underages. Results show that the multi-view learning combining scrolls, swipes, and pinch-to-zoom gestures, achieves the best ROC AUC (0.92) and accuracy (88%) scores

The Conundrum of Success in Music: Playing it or Talking About it?

Nowadays social media are the main means for conducting discussions and sharing opinions. The huge amount of information generated by social media users is helpful for predicting outcomes of real-world events in different fields, including business, politics and the entertainment industry. In this paper, we studied the possibility of forecasting the success of music albums by analyzing heterogeneous data sources spanning from social media (Twitter, Instagram and Facebook) to mainstream American newspapers (e.g., New York Times, Rolling Stones). The idea is to exploit music albums' pre-release hype and post-release approval to predict the album's rank with reference to the well-known Billboard 200 album chart, which tabulates the weekly popularity of music albums in the USA. To predict the success of a music album, that is its rank in the chart, we identified metrics based on the messages' posting trend, the variation of the sentiment associated to such messages, the number of followers of the album's author, and the importance of the people who talk about it. To evaluate the effectiveness of the proposed metrics we have compared the prediction performances of several models based on supervised learning approaches among those most used in literature. As a result, we obtained that the Random Forest approach is able to predict the music album rank in the Billboard 200 Chart with an expected accuracy of 97%. As a further validation, using this specific model, we also conducted an additional real usage test obtaining an almost matching result (accuracy of 94%)

22nd International Conference Information Visualisation, IV 2018

22nd International Conference Information Visualisation, IV 2018, Fisciano, Italy, July 10-13, 201

A Color-based Visualization Approach to understand harmonic structures of Musical Compositions

Music expertise is the ability to understand the structural elements of music compositions by reading musical scores or even by simply listening to music performance. Although the most common way to learn music is through the study of musical scores, this approach is demanding in terms of learning ability, given the required implicit knowledge of music theoretical notations and concepts. In this work we define a two-level color-based approach, that exploits graphical visualization techniques to represent data structures of classical music, and to perform harmonic analysis of musical compositions. Our main goal is to make easier and very quick the study of classical notations (recognized as a tedious and difficult task in the field), by providing individuals with a mechanism that clarifies complex relationships in music using visual clues. We performed a preliminary study to evaluate the effectiveness of our approach as well as participants' perceptions about its usefulness and pleasantness. The results of the study provided us with overall positive feedback about the effectiveness of our approach as well as further directions to explore