New Aspects of Synchronous Data Flow and Distributed Development in Digital Audio: the Case of Ciaramella Programming Language

A fundamental challenge in audio software engineering is the tension between expressive, high-level programming tools and the high-performance, low-level languages required for real-time audio processing. This gap often compels developers to trade the clarity and productivity of high-level abstractions for computational efficiency. This thesis confronts these challenges through a constructive research methodology, presenting a new programming ecosystem for audio Digital Signal Processing. The central hypothesis is that a strict adherence to the formal semantics of the Synchronous Data Flow (SDF) model can provide the foundation for a language and toolchain that is simultaneously expressive, robust, and efficient. The core contributions of this work are threefold. First, it introduces Ciaramella, a declarative programming language grounded in SDF. Its novel compilation strategy, which defers computability analysis to a late stage of compilation, allows for a natural and modular construction of highly interdependent systems, such as Wave Digital Filters, that are challenging to implement in other dataflow languages. Second, this work presents a novel framework for stateful conditional branching within the strict SDF model. This extends the paradigm to support a wider class of algorithms, resolving a long-standing limitation without compromising the determinism and static schedulability essential for real-time audio. Finally, to modernize the development lifecycle, the thesis introduces PIPES, a language-agnostic, networked protocol for the remote compilation and deployment of audio code. This enables a rapid, iterative workflow by decoupling the coding environment from the build and execution targets.