CIRQUE DE LA VOIX: VOCAL PERFORMANCES FOR THE 21st CENTURY AUDIENCE

This dissertation addresses the growing need to entice people to attend a classical solo vocal recital by incorporating thematic programming, multi-media presentations, collaborations and innovative marketing. It comprises four programs that use the above tactics, creating live performances of classical vocal music that appeal to the attention deficient 21st-century audience. Each program focuses on repertoire appropriate for the male alto voice and includes elements of spoken word, visual imagery and for movement through collaborations with actors, singers, dancers, designers and visual artists. Program one (March 1, 2004), La Voix Humaine: The Life of an Englishwoman in Music, Poetry, & Art, outlines the life of a fictitious Englishwoman through a self-composed narration, spoken by an actress, a Power Point presentation of visual art by 20th-century English artists and musical commentary provided by the collaboration of a vocalist and a pianist. Program two (October 15, 2004), La Voix Thfrmatique: Anima - Music that Moves, is a program of pieces ranging from the 14th- to the 20th-centuries of which half are choreographed by members of the University of Maryland Dance Department. Program three is a lecture recital entitled L 'Haute Voix: Identifying the High Male Voice and Appropriate Repertoire which is presented in collaboration with three singers, a pianist, a harpsichordist and a cellist. Program four, La Voix Dramatique: Opera Roles for the Countertenor Voice, comprises performances of George Frederic Handel's Giulio Cesare in Egitto (1724) in collaboration with the Maryland Opera Studio and the Clarice Smith Performing Arts Center (Leon Major, director; Kenneth Merrill, conductor). There are two performances each of the title role, Cesare (April 15 & 17, 2005), and his nemesis, Tolomeo (April 21 & 23,2005). All programs are documented in a digital audio format available on compact disc and are accompanied by program notes also available in digital format. Programs two and four are also documented in digital video format available on digital video disc.

MUSICAL THEATER AND THE CLASSICAL VOICE: CROSSOVER SINGING IN THE TWENTIETH CENTURY

This dissertation explores the transformation of opera comique (as represented by the opera Carmen) and the impact of verismo style (as represented by the opera La Boheme) upon the development of operetta, American musical theater and the resultant change in vocal style. Late nineteenth-century operetta called for a classically trained soprano voice with a clear vibrato. High tessitura and legato were expected although the quality of the voice was usually lighter in timbre. The dissertation comprises four programs that explore the transformation of vocal and compositional style into the current vocal performance practice of American musical theater. The first two programs are operatic roles and the last two are recital presentations of nineteenth- and twentieth- century operetta and musical theater repertoire. Program one, Carmen, was presented on July 26, 2007 at the Marshall Performing Arts Center in Duluth, MN where I sang the role of Micaela. Program two, La Boheme, was presented on May 24,2008 at Randolph Road Theater in Silver Spring, MD where I sang the role of Musetta. Program three, presented on December 2, 2008 and program four, presented on May 10, 2009 were two recitals featuring operetta and musical theater repertoire. These programs were heard in the Gildenhorn Recital Hall at the Clarice Smith Performing Arts Center in College Park, MD. Programs one and two are documented in a digital video format available on digital video disc. Programs three and four are documented in a digital audio format available on compact disc. All programs are accompanied by program notes also available in digital format.

A Video Game-Integrated Electromyography Biofeedback Device for Use in Physical Therapy

We present a novel system to be used in the rehabilitation of patients with forearm injuries. The system uses surface electromyography (sEMG) recordings from a wireless sleeve to control video games designed to provide engaging biofeedback to the user. An integrated hardware/software system uses a neural net to classify the signals from a user’s muscles as they perform one of a number of common forearm physical therapy exercises. These classifications are used as input for a suite of video games that have been custom-designed to hold the patient’s attention and decrease the risk of noncompliance with the physical therapy regimen necessary to regain full function in the injured limb. The data is transmitted wirelessly from the on-sleeve board to a laptop computer using a custom-designed signal-processing algorithm that filters and compresses the data prior to transmission. We believe that this system has the potential to significantly improve the patient experience and efficacy of physical therapy using biofeedback that leverages the compelling nature of video games.

THE EFFECTIVENESS OF POINT-OF-VIEW VIDEO MODELING IN TEACHING SOCIAL INITIATION SKILLS TO CHILDREN WITH AUTISM SPECTRUM DISORDERS

Deficits in social communication and interaction have been identified as distinguishing impairments for individuals with an autism spectrum disorder (ASD). As a pivotal skill, the successful development of social communication and interaction in individuals with ASD is a lifelong objective. Point-of-view video modeling has the potential to address these deficits. This type of video involves filming the completion of a targeted skill or behavior from a first-person perspective. By presenting only what a person might see from his or her viewpoint, it has been identified to be more effective in limiting irrelevant stimuli by providing a clear frame of reference to facilitate imitation. The current study investigated the use of point-of-view video modeling in teaching social initiations (e.g., greetings). Using a multiple baseline across participants design, five kindergarten participants were taught social initiations using point-of-view video modeling and video priming. Immediately before and after viewing the entire point-of-view video model, the participants were evaluated on their social initiations with a trained, typically developing peer serving as a communication partner. Specifically, the social initiations involved participants’ abilities to shift their attention toward the peer who entered the classroom, maintain attention toward the peer, and engage in an appropriate social initiation (e.g., hi, hello). Both generalization and maintenance were tested. Overall, the data suggest point-of-view video modeling is an effective intervention for increasing social initiations in young students with ASD. However, retraining was necessary for acquisition of skills in the classroom environment. Generalization in novel environments and with a novel communication partner, and generalization to other social initiation skills was limited. Additionally, maintenance of gained social initiation skills only occurred in the intervention room. Despite the limitations of the study and variable results, there are a number of implications moving forward for both practitioners and future researchers examining point-of-view modeling and its potential impact on the social initiation skills of individuals with ASD.

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.