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School of Music

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Now showing 1 - 4 of 4
  • Item
    Automatic accompaniment of vocal melodies in the context of popular music
    (Georgia Institute of Technology, 2009-04-08) Cao, Xiang
    A piece of popular music is usually defined as a combination of vocal melody and instrumental accompaniment. People often start with the melody part when they are trying to compose or reproduce a piece of popular music. However, creating appropriate instrumental accompaniment part for a melody line can be a difficult task for non-musicians. Automation of accompaniment generation for vocal melodies thus can be very useful for those who are interested in singing for fun. Therefore, a computer software system which is capable of generating harmonic accompaniment for a given vocal melody input has been presented in this thesis. This automatic accompaniment system uses a Hidden Markov Model to assign chord to a given part of melody based on the knowledge learnt from a bank of vocal tracks of popular music. Comparing with other similar systems, our system features a high resolution key estimation algorithm which is helpful to adjust the generated accompaniment to the input vocal. Moreover, we designed a structure analysis subsystem to extract the repetition and structure boundaries from the melody. These boundaries are passed to the chord assignment and style player subsystems in order to generate more dynamic and organized accompaniment. Finally, prototype applications are discussed and the entire system is evaluated.
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    Generative rhythmic models
    (Georgia Institute of Technology, 2009-04-08) Rae, Alexander
    A system for generative rhythmic modeling is presented. The work aims to explore computational models of creativity, realizing them in a system designed for realtime generation of semi-improvisational music. This is envisioned as an attempt to develop musical intelligence in the context of structured improvisation, and by doing so to enable and encourage new forms of musical control and performance; the systems described in this work, already capable of realtime creation, have been designed with the explicit intention of embedding them in a variety of performance-based systems. A model of qaida, a solo tabla form, is presented, along with the results of an online survey comparing it to a professional tabla player's recording on dimensions of musicality, creativity, and novelty. The qaida model generates a bank of rhythmic variations by reordering subphrases. Selections from this bank are sequenced using a feature-based approach. An experimental extension into modeling layer- and loop-based forms of electronic music is presented, in which the initial modeling approach is generalized. Starting from a seed track, the layer-based model utilizes audio analysis techniques such as blind source separation and onset-based segmentation to generate layers which are shuffled and recombined to generate novel music in a manner analogous to the qaida model.
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    Storage in Collaborative Networked Art
    (Georgia Institute of Technology, 2009) Freeman, Jason
    This chapter outlines some of the challenges and opportunities associated with storage in networked art. Using comparative analyses of collaborative networked music as a starting point, this chapter explores how networked storage can transform the relationship between composition and improvisation; how it can influence network designs focused on shared material or shared control; how it can actively and autonomously manipulate its own contents; how it can circumvent problems of network latency and facilitate asynchronous collaboration; and how it can exist as a core component of a work’s design without being at the core of every user’s experience.
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    Hubs and homogeneity: improving content-based music modeling
    (Georgia Institute of Technology, 2008-04-01) Godfrey, Mark Thomas
    With the volume of digital media available today, automatic music recommendation services have proven a useful tool for consumers, allowing them to better discover new and enjoyable music. Typically, this technology is based on collaborative filtering techniques, employing human-generated metadata to base recommendations. Recently, work in content-based recommendation systems have emerged in which the audio signal itself is analyzed for relevant musical information from which models are built that attempt to mimic human similarity judgments. The current state-of-the-art for content-based music recommendation uses a timbre model based on MFCCs calculated on short segments of tracks. These feature vectors are then modeled using GMMs (Gaussian mixture models). GMM modeling of frame-based MFCCs has been shown to perform fairly well on timbre similarity tasks. However, a common problem is that of hubs , in which a relative small number of songs falsely appear similar to many other songs, significantly decreasing the accuracy of similarity recommendations. In this thesis, we explore the origins of hubs in timbre-based modeling and propose several remedies. Specifically, we find that a process of model homogenization, in which certain components of a mixture model are systematically removed, improves performance as measured against several ground-truth similarity metrics. Extending the work of Aucouturier, we introduce several new methods of homogenization. On a subset of the uspop data set, model homogenization improves artist R-precision by a maximum of 3.5% and agreement to user collection co-occurrence data by 7.4%. We also find differences in the effectiveness of the various homogenization methods for hub reduction, with the proposed methods providing the best results. Further, we extend the modeling of frame-based MFCC features by using a kernel density estimation approach to non-parametric modeling. We find that such an approach significantly reduces the number of hubs (by 2.6% of the dataset) while improving agreement to ground-truth by 5% and slightly improving artist R-precision as compared with the standard parametric model. Finally, to test whether these principles hold for all musical data, we introduce an entirely new data set consisting of Indian classical music. We find that our results generalize here as well, suggesting that hubness is a general feature of timbre-based similarity music modeling and that the techniques presented to improve this modeling are effective for diverse types of music.