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Doctor of Philosophy with a Major in Music Technology

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https://hdl.handle.net/1853/73273
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Now showing 1 - 3 of 3
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    Addressing the data challenge in automatic drum transcription with labeled and unlabeled data
    (Georgia Institute of Technology, 2018-07-23) Wu, Chih-Wei
    Automatic Drum Transcription (ADT) is a sub-task of automatic music transcription that involves the conversion of drum-related audio events into musical notations. While noticeable progress has been made in the past by combining pattern recognition methods with audio signal processing techniques, many systems are still impeded by the lack of a meaningful amount of labeled data to support the data-driven algorithms. To address this data challenge in ADT, this work presents three approaches. First, a dataset for ADT tasks is created using a semi-automatic process that minimizes the workload of human annotators. Second, an ADT system that requires minimum training data is designed to account for the presence of other instruments (e.g., non-percussive or pitched instruments). Third, the possibility of improving generic ADT systems with a large amount of unlabeled data from online resources is explored. The main contributions of this work include the introduction of a new ADT dataset, the methods for realizing ADT systems under the constraint of data insufficiency, and a scheme for data-driven methods to benefit from the abundant online resources and might have impact on other audio and music related tasks traditionally impeded by small amounts of labeled data.
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    The algorithmic score language: Extending common western music notation for representing logical behaviors
    (Georgia Institute of Technology, 2018-05-22) Martinez Nieto, Juan Carlos
    This work proposes extensions to Western Music Notation so it can play a dual role: first as a human-readable representation of the music performance information in the context of live-electronics, and second as a programming language which is executed during the live performance of a piece. This novel approach simplifies the compositional workflow, the communication with performers, the musical analysis, and the actual performance of scored pieces that involve computer interactions. Extending Western Music Notation as a programming language creates musical scores which encode music information for performance that is human-readable, cohesive, self-contained and sustainable, making the interactive music genre attractive to a wide spectrum of composers and performers of new music. A collection of pieces was composed and performed based on the new extended notation and some repertoire pieces were transcribed enabling the syntax evaluation in the context of different compositional aesthetics. The results of this research created a unique approach to composition and performance of interactive music that is supported by technology and founded in traditional music practices that have been used for centuries.
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    Towards an embodied musical mind: Generative algorithms for robotic musicians
    (Georgia Institute of Technology, 2017-04-19) Bretan, Peter Mason
    Embodied cognition is a theory stating that the processes and functions comprising the human mind are influenced by a person's physical body. The theory of embodied musical cognition holds that a person's body largely influences his or her musical experiences and actions. This work presents multiple frameworks for computer music generation as it pertains to robotic musicianship such that the musical decisions result from a joint optimization between the robot's physical constraints and musical knowledge. First, a generative framework based on hand-designed higher level musical concepts and the Viterbi beam search algorithm is described. The system allows for efficient and autonomous exploration on the relationship between music and physicality and the resulting music that is contingent on such a connection. It is evaluated objectively based on its ability to plan a series of sound actuating robotic movements (path planning) that minimize risk of collision, the number of dropped notes, spurious movements, and energy expenditure. Second, a method for developing higher level musical concepts (semantics) based on machine learning is presented. Using strategies based on neural networks and deep learning we show that it is possible to learn perceptually meaningful higher-level representations of music. These learned musical ``embeddings'' are applied to an autonomous music generation system that utilizes unit selection. The embeddings and generative system are evaluated based on objective ranking tasks and a subjective listening study. Third, the method for learning musical semantics is extended to a robot such that its embodiment becomes integral to the learning process. The resulting embeddings simultaneously encode information describing both important musical features and the robot's physical constraints.