TROMBONE MUSIC OF SCANDINAVIA: A STUDY OF TROMBONE SOLO AND CHAMBER WORKS FROM SWEDEN, NORWAY, AND DENMARK

This dissertation project aims to establish Scandinavian trombone solo and chamber works as a major contribution to the trombone repertoire. From the late 19th century to modern day, Scandinavian composers have produced a steady output of trombone works of substantial musical quality. Deep-rooted in the traditions of strong military wind bands, Scandinavia has also produced an unusual number of trombone virtuosos, ranging from those holding positions in leading orchestras, and internationally renowned pedagogues, to trombonists enjoying careers as soloists. In this study I propose that it is the symbiotic relationship between strong performers and traditionally nationalist composers that created the fertile environment for the large number of popular trombone solo and chamber repertoire not seen in any other region besides the Paris Conservatory and its infamous test pieces. I also interpret the selected repertoire through the prism of nationalism and influence of folk music, and convey that the allure of the mystic Nordic folk influences enhances the appeal of the Scandinavian trombone repertoire to world-wide audiences and performers. The dissertation project was realized over three solo recitals, each showcasing the music of Sweden, Norway, and Denmark respectively. For each program, I looked to choose a standard work from the trombone solo repertoire, a work written for or by a native virtuoso, and a lesser-known work that warrants the attention of other performers for its musical qualities. The recital of Swedish music presented Mandrake in the Corner by Christian Lindberg, Subadobe by Frederik Högberg, A Christian Song by Jan Sandström, and Concertino for trombone and strings by Lars-Erik Larsson. The recital of Norwegian music presented Concerto for Trombone op. 76 by Egil Hovland, Ordner Seg by Øystein Baadsvik, Elegi by Magne Amdahl, and Concerto in F major by Ole Olsen. The recital of Danish music presented Rapsodia Borealis by Søren Hyldgaard, Madrigal by Bo Gunge, Romance for trombone and piano by Axel Jørgensen, Concerto for trombone by Launy Grøndahl, and Three Swedish Tunes by Mogens Andresen. Through the performance of works from these three countries, the dissertation establishes Scandinavia as a rich source of solo trombone repertoire perpetuated by nationalist composers and virtuosos, as well as providing a brief survey of Scandinavian trombone works of various instrumentation and difficulty levels to be enjoyed by student, professional, and amateur performers and their audience.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.