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, otherwise known as the coronavirus, has precipitated the world into a pandemic that has infected, as of the time of writing, more than 10 million persons worldwide and caused the death of more than 500,000 persons. Early symptoms of the virus include trouble breathing, fever and fatigue and over 60% of people experience a dry cough. Due to the devastating impact of COVID-19 and the tragic loss of lives, it is of the utmost urgency to develop methods for the early detection of the disease that may help limit its spread as well as aid in the development of targeted solutions. Coughs and other vocal sounds contain pulmonary health information that can be used for diagnostic purposes, and recent studies in chaotic dynamics have shown that nonlinear phenomena exist in vocal signals. The present work investigates the use of symbolic recurrence quantification measures with MFCC features for the automatic detection of COVID-19 in cough sounds of healthy and sick individuals. Our performance evaluation reveals that our symbolic dynamics measures capture the complex dynamics in the vocal sounds and are highly effective at discriminating sick and healthy coughs. We apply our method to sustained vowel 'ah' recordings, and show that our model is robust for the detection of the disease in sustained vowel utterances as well. Furthermore, we introduce a robust novel method of informative undersampling using information rate to deal with the imbalance in our dataset, due to the unavailability of an equal number of sick and healthy recordings. The proposed model achieves a mean classification performance of 97% and 99%, and a mean F 1 -score of 91% and 89% after optimization, for coughs and sustained vowels, respectively.

We present an exhaustive review of research on automatic classification of sounds from musical instruments. Two different but complementary approaches are examined, the perceptual approach and the taxonomic approach. The former is targeted to derive perceptual similarity functions in order to use them for timbre clustering and for searching and retrieving sounds by timbral similarity. The latter is targeted to derive indexes for labeling sounds after culture-or user-biased taxonomies. We review the relevant features that have been used in the two areas and then we present and discuss different techniques for similarity-based clustering of sounds and for classification into pre-defined instrumental categories.

We describe variable markov models we have used for statistical learning of musical sequences, then we present the factor oracle, a data structure proposed by Crochemore & al for string matching. We show the relation between this structure and the previous models and indicate how it can be adapted for learning musical sequences and generating improvisations in a real-time context. Modeling musical sequencesStatistical modeling of musical sequences has been experimented since the very beginnings of musical informatics (see [Con03] for a review and criticism of most existing models, and [Zic87] for one of the first available real-time interactive system). The idea behind context models, which we are mostly interested in here, is that events in a musical piece can be predicted from the sequence of preceding events. The operational property of such models is to provide the conditional probability distribution over an alphabet given a sequence. For example, if w is musical sequence, r a symbol belonging to the musical alphabet P , P(r|w) is the probability that r will follow w, i.e. the probability of wr given w. This distribution P will be used for generating new sequences or for computing the probability of a given one. First experiments in context based modeling made intensive use of Markov chains.[Ron96] explain that this idea dates back to Shannon : complex sequences do not have obvious underlying source, however, they exhibit a property called short memory property by the authors; there exists a certain memory lengh L such that the conditional probability distribution on the next symbol r does not change significantly if we condition it on suffixes of w longer than L. In the case of Markov chains, L is the order. However, the size of Markov chains is O(| P | L ), so only low order models have been actually experimented.To cope with the model order problem, in earlier works [Dub98, Dub02, Dub03, Ass99] we have proposed a method for building musical style analyzers and generators based on several algorithms for prediction of discrete sequences using Variable Markov Models (VMM). The class of these algorithms is large and we focused mainly on two variants of predictors -universal prediction based on Incremental Parsing (IP) and prediction based on Probabilistic Suffix Trees (PST).The IP method is derived from Information Theory. J. Ziv and A. Lempel [Ziv78] first suggested the core of this method called Incremental Parsing in the context of lossless compression research. IP builds a dictionary of distinct motifs by making a single left to right traversal of a sequence, sequentially adding to a dictionary every new phrase that differs by a single last character from the longest match that already exists in the dictionary. Using a tree representation for the dictionary, every node is associated with a string, whose characters appear as labels on the arcs that lead from the root to that node. Each time the parsing algorithm reaches a longest-match node it means that the node's string has already occurred in the sequen...

Building on the work of Leonard B. Meyer, 3 researchers commonly agree that expectations based on recent past context guide musical perception. In music applications, exactly how we make musical Research using statistical and information-theoretic tools provides inference and prediction models that, to a certain extent, can generate new musical works imitating the style of the great masters.

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