Abstract
This paper describes the creative and technical processes behind earGram, an application created with Pure Data for real-time concatenative sound synthesis. The system encompasses four generative music strategies that automatically rearrange and explore a database of descriptor-analyzed sound snippets (corpus) by rules other than their original temporal order into musically coherent outputs. Of note are the system’s machine-learning capabilities as well as its visualization strategies, which constitute a valuable aid for decisionmaking during performance by revealing musical patterns and temporal organizations of the corpus.
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Bernardes, G., Guedes, C., Pennycook, B. (2013). EarGram: An Application for Interactive Exploration of Concatenative Sound Synthesis in Pure Data. In: Aramaki, M., Barthet, M., Kronland-Martinet, R., Ystad, S. (eds) From Sounds to Music and Emotions. CMMR 2012. Lecture Notes in Computer Science, vol 7900. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41248-6_7
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DOI: https://doi.org/10.1007/978-3-642-41248-6_7
Publisher Name: Springer, Berlin, Heidelberg
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