A Project on Developing Catholic Liturgical Music in Vietnam

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Singing Through Struggle: Music as a Mode of Cultural Exchange in African American Border City Churches After Emancipation, 1862-1890

This project investigates how religious music, invested with symbolic and cultural meaning, provided African Americans in border city churches with a way to negotiate conflict, assert individual values, and establish a collective identity in the post- emancipation era. In order to focus on the encounter between former slaves and free Blacks, the dissertation examines black churches that received large numbers of southern migrants during and after the Civil War. Primarily a work of history, the study also employs insights and conceptual frameworks from other disciplines including anthropology and ritual studies, African American studies, aesthetic theory, and musicology. It is a work of historical reconstruction in the tradition of scholarship that some have called "lived religion." Chapter 1 introduces the dissertation topic and explains how it contributes to scholarship. Chapter 2 examines social and religious conditions African Americans faced in Baltimore, MD, Philadelphia, PA, and Washington, DC to show why the Black Church played a key role in African Americans' adjustment to post-emancipation life. Chapter 3 compares religious slave music and free black church music to identify differences and continuities between them, as well as their functions in religious settings. Chapters 4, 5, and 6 present case studies on Bethel African Methodist Episcopal Church (Baltimore), Zoar Methodist Episcopal Church (Philadelphia), and St. Luke’s Protestant Episcopal Church (Washington, DC), respectively. Informed by fresh archival materials, the dissertation shows how each congregation used its musical life to uphold values like education and community, to come to terms with a shared experience, and to confront or avert authority when cultural priorities were threatened. By arguing over musical choices or performance practices, or agreeing on mutually appealing musical forms like the gospel songs of the Sunday school movement, African Americans forged lively faith communities and distinctive cultures in otherwise adverse environments. The study concludes that religious music was a crucial form of African American discourse and expression in the post-emancipation era. In the Black Church, it nurtured an atmosphere of exchange, gave structure and voice to conflict, helped create a public sphere, and upheld the values of black people.

"Not Without My Ancestors": A Christological Case Study of the Shona Ancestor Cult of Zimbabwe

The issue of ancestors has been controversial since the first encounters of Christianity with Shona religion. It remains a major theological problem that needs to be addressed within the mainline churches of Zimbabwe today. Instead of ignoring or dismissing the ancestor cult, which deeply influences the socio-political, religious, and economic lives of the Shona, churches in Zimbabwe should initiate a Christology that is based on it. Such a Christology would engage the critical day-to-day issues that make the Shona turn to their ancestors. Among these concerns are daily protection from misfortune, maintaining good health and increasing longevity, successful rainy seasons and food security, and responsible governance characterized by economic and political stability. Since the mid-16th century arrival of Jesuit missionaries in the Mutapa Kingdom, the Church has realized that many African Christians resorted to their ancestors in times of crisis. Although both Catholic and Protestant missionaries from the 1700s through the early 1900s fiercely attacked Shona traditional beliefs as superstitious and equated ancestors with evil spirits, the cult did not die. Social institutions, such as schools and hospitals provided by missionaries, failed to eliminate ancestral beliefs. Even in the 21st century, many Zimbabweans consult their ancestors. The Shona message to the church remains "Not without My Ancestors." This dissertation examines the significance of the ancestors to the Shona, and how selected denominations and new religious movements have interpreted and accommodated ancestral practices. Taking the missiological goal of "self-theologizing" as the framework, this dissertation proposes a "tripartite Christology" of "Jesus the Family Ancestor", "Jesus the Tribal Ancestor," and "Jesus the National Ancestor," which is based on the Shona "tripartite ancestrology." Familiar ecclesiological and liturgical language, idioms, and symbols are used to contribute to the wider Shona understanding of Jesus as the ancestor par excellence, in whom physical and spiritual needs-including those the ordinary ancestors fail to meet-are fulfilled.

Multi-scale 3D Scene Flow from Binocular Stereo Sequences

Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation approaches. This paper describes an alternative formulation for dense scene flow estimation that provides convincing results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. To handle the aperture problems inherent in the estimation task, a multi-scale method along with a novel adaptive smoothing technique is used to gain a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization-two problems commonly associated with basic multi-scale approaches. Internally, the framework generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than standard stereo and optical flow methods allow. Experiments with synthetic and real test data demonstrate the effectiveness of the approach.

Multi-Scale 3D Scene Flow from Binocular Stereo Sequences

Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene flow estimation that provides reliable results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than previous methods allow. To handle the aperture problems inherent in the estimation of optical flow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization – two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.

Music Maker – A Camera-based Music Making Tool for Physical Rehabilitation

The therapeutic effects of playing music are being recognized increasingly in the field of rehabilitation medicine. People with physical disabilities, however, often do not have the motor dexterity needed to play an instrument. We developed a camera-based human-computer interface called "Music Maker" to provide such people with a means to make music by performing therapeutic exercises. Music Maker uses computer vision techniques to convert the movements of a patient's body part, for example, a finger, hand, or foot, into musical and visual feedback using the open software platform EyesWeb. It can be adjusted to a patient's particular therapeutic needs and provides quantitative tools for monitoring the recovery process and assessing therapeutic outcomes. We tested the potential of Music Maker as a rehabilitation tool with six subjects who responded to or created music in various movement exercises. In these proof-of-concept experiments, Music Maker has performed reliably and shown its promise as a therapeutic device.

AIRSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.

ARTSCENE: A Neural System for Natural Scene Classification

How do humans rapidly recognize a scene? How can neural models capture this biological competence to achieve state-of-the-art scene classification? The ARTSCENE neural system classifies natural scene photographs by using multiple spatial scales to efficiently accumulate evidence for gist and texture. ARTSCENE embodies a coarse-to-fine Texture Size Ranking Principle whereby spatial attention processes multiple scales of scenic information, ranging from global gist to local properties of textures. The model can incrementally learn and predict scene identity by gist information alone and can improve performance through selective attention to scenic textures of progressively smaller size. ARTSCENE discriminates 4 landscape scene categories (coast, forest, mountain and countryside) with up to 91.58% correct on a test set, outperforms alternative models in the literature which use biologically implausible computations, and outperforms component systems that use either gist or texture information alone. Model simulations also show that adjacent textures form higher-order features that are also informative for scene recognition.