Concerto russe : pour le violon avec accompagnement d'orchestre ou de piano. : op. 29.

For violin and piano.

Graduate Violin Recital

Sonata for Solo Violin in G-minor, J. S. Bach (1685-1750) Adagio Fuga Siciliana Presto Sonata for Violin and Piano in G-major, L. Beethoven (1770-1827) Allegro assai Tempo di Menuetto Allegro vivace Sonata for Violin and Piano in A-major, G. Faure (1845-1924) Allegro molto Andante Allegro vivo Allegro quasi presto Jennifer Snyder, piano

Evolution of classifiers for pitch estimation of piano music using cartesian genetic programming

Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP).We take advantage of evolutionary algorithms, in particular CGP, to explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.

The Effects of Live, Taped and No Music on People Experiencing Posttraumatic Amnesia

This study compared the effects of live, taped, and no music, on agitation and orientation levels of people experiencing posttraumatic amnesia (PTA). Participants (N = 22) were exposed to all 3 conditions, twice over 6 consecutive days. Songs used in the live and taped music conditions were identical and were selected based on participants' own preferred music. Pre and posttesting was conducted for each condition using the Agitated Behavior Scale (Corrigan, 1989) and the Westmead PTA Scale (Shores, Marosszeky, Sandanam, Batchelor, 1986). Participants' memory for the music used was also tested and compared with their memory for pictorial material presented in the Westmead PTA Scale. Results indicate that music significantly reduced agitation (p

Paul Auster and the music of change: building new stories

In the work of Paul Auster (Newark, 1947 - ), we find two main themes: the sense of loss and existential drift and the loneliness of the individual fully committed to the work of writing, as if he had been confined to the book that commands his life. However, this second theme is clearly the dominant one because the character's space of solitude may include its own wandering, because this wandering is also often performed inside the four walls of a room, just like it is narrated inside the space of the page and the book. Both in his poetry, essays and fiction, Auster seems to face the work of writing as an actual physical effort of effective construction, as if the words that are aligned in the poem-text were stones to place in a row when building a wall or some other structure in stone.

Schubert i les seves composicions per a violí

Amb aquest projecte he volgut aproximar-me al repertori de cambra per violí i piano del compositor vienès Franz Schubert. Les tres obres escollides són poc habituals a les sales de concert, en part per la seva complexitat tècnica i interpretativa, i representen tres propostes ben contrastants en la seva trajectòria compositiva. Es tracte de la introspectiva Sonatina en La menor D. 385, l’eloqüent Rondó Brillant en Si menor D. 895 i la seva obra de maduresa, la gran Fantasia en Do Major D. 934. Gràcies a l’anàlisi musical i la recerca del seu context cultural i artístic, juntament amb la meva experiència en l’instrument, he guanyat una percepció i comprensió molt més profunda de les seves obres, creixent com a persona i intèrpret amb el procés.

Concerto for Violin and Orchestra No. 1 in A minor : Add-a-soloist ; you play the solo part

r1955.

Romance No. 2 in F Major for Violin and Orchestra

Soitinnus: Viulu, ork.

Twelve variations in G major on a theme from Handel's "Judas Maccabaeus" for Violoncello and Piano

Soitinnus: Sello, piano.

Concerto for Violin and Orchestra No. 2 in E major : Add-a-soloist ; you play the solo part

r1955.

Sonata for Cello and Piano No. 1 in E Minor, Op. 38

Soitinnus: Sello, piano.