Sound recycling from public databases: Another BigData approach to sound collections

Abstract

Discovering new sounds from large databases or Internet is a tedious task. Standard search tools and manual exploration fails to manage the actual amount of information available. This paper presents a new approach to the problem which takes advantage of grown technologies like Big Data and Machine Learning, keeping in mind compositional concepts and focusing on artistic performances. Among several different distributed systems useful for music experimentation, a new workflow is proposed based on analysis techniques from Music Information Retrieval (MIR) combined with massive online databases, dynamic user interfaces, physical controllers and real-time synthesis. Based on Free Software tools and standard communication protocols to classify, cluster and segment sound. The control architecture allows multiple clients request the API services concurrently enabling collaborative work. The resulting system can retrieve well defined or pseudo-aleatory audio samples from the web, mix and transform them in real-time during a live-coding performance, play like another instrument in a band, as a solo artist combined with visual feedback or working alone as automated multimedia installation.