[05] Andrew Telichan: Mikenko

CitiGram (sensory rozmístěné na newyorkském Manhattanu) - Foto: Michal Rataj

CitiGram (sensory rozmístěné na newyorkském Manhattanu)Foto: Michal Rataj

rAdioCUSTICA selected 2015 | 03:55

 

 

Andrew Telichan: Mikenko

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Using a system developed in the Max environment, Mikenko is designed through the detection and deconstruction of human-voice fragments from an ongoing soundscape recording, playing these fragments back in a random order and at varying speeds. For this particular piece, the recorded soundscape entails a series of recordings taken in Times Square, New York. The more playback speech the system detects, the more individual syllables and phonemes it catalogues and plays back, thereby creating feedback loops that continuously intensify and diminish over time. During this process, the speech fragments are sent through various levels of spectral and granular re-synthesis, creating a diverse – and constantly (de)-evolving – output, while envelope followers read time and amplitude information of the vocal fragments, which are then used to control parameters of external synthesis engines. Additional parameters – including granular size, granular playback speed, and panning – are controlled by real-time data streams related to the soundscape recording itself, gathered and pushed via NYU’s CityGram network. The overall result is a mix of textural sonorities composed of voices and synthetic sounds that grow and develop over time.  

 

Tae Hong Park: Sinescapes

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Composition is part of Citygram-Sound Project as a collaboration between NYU Steinhardt, NYU CUSP, and CalArts. The Citygram Project is a large-scale project that began in 2011. Citygram aims to deliver a real-time visualization/mapping system focusing on non-ocular energies through scale-accurate, non-intrusive, and data-driven interactive digital maps. The first iteration, Citygram One, focuses on exploring spatio-acoustic energies to reveal meaningful information including spatial loudness, traffic patterns, noise pollution, and emotion/mood through audio signal processing and machine learning techniques. Citygram aims to create a model for visualizing and applying computational techniques to produce metrics and further our knowledge of cities such as NYC and LA, while lately also the city of Prague has been included. 

Související weby

citygram.smusic.nyu.edu

Autor:  Michal Rataj

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