conTorchionist: A flexible nomadic library for exploring machine listening/learning in multiple platforms, languages, and time-contexts - Padovani, José Henrique; Oliveira, Vinícius Cesar de (Anais do Simpósio Brasileiro de Computação Musical; 2025: Anais do XIX Simpósio Brasileiro de Computação MusicalDO - 10.5753/sbcm.2025.14270, 2025)
The concept of ‘machine listening’ has profoundly shaped the development of interactive music systems, evolving from symbolic MIDI processing to a wide range of audio analysis techniques. While the recent proliferation of neural networks has greatly expanded these capabilities, it has also introduced complex processes that often function as opaque ‘black boxes’, limiting creative exploration. To address this, we present ‘conTorchionist’: a flexible, nomadic library designed to foster a more transparent approach to machine listening and learning. Built upon libtorch/PyTorch, it features a single shared core with dedicated interfaces for diverse environments, including Pure Data, Max, SuperCollider, and Python. This architecture unifies real-time and non-real-time workflows, leveraging libtorch’s strengths in GPU-accelerated audio analysis, signal processing, and neural network inference. Ultimately, conTorchionist provides an adaptable toolset that empowers researchers and artists to seamlessly bridge different platforms, languages, and time-contexts in their creative and investigative work.