Abstract
For its versatility, Python has become one of the most popular programming languages. In spite of its possibility to straightforwardly link native code with powerful libraries for scientific computing, the use of Python for real-time sound applications development is often neglected in favor of alternative programming languages, which are tailored to the digital music domain. This article introduces Python as a real-time software programming tool to interested readers, including Python developers who are new to the real time or, conversely, sound programmers who have not yet taken this language into consideration. Cython and Numba are proposed as libraries supporting agile development of efficient software running at machine level. Moreover, it is shown that refactoring few critical parts of the program under these libraries can dramatically improve the performances of a sound algorithm. Such improvements can be directly benchmarked within Python, thanks to the existence of appropriate code parsing resources. After introducing a simple sound processing example, two algorithms that are known from the literature are coded to show how Python can be effectively employed to program sound software. Finally, issues of efficiency are mainly discussed in terms of latency of the resulting applications. Overall, such issues suggest that the use of real-time Python should be limited to the prototyping phase, where the benefits of language flexibility prevail on low latency requirements, for instance, needed during computer music live performances.
Highlights
Among the many definitions, the development of computer music applications has been qualified as “the expression of compositional or signal processing ideas” [1]
Because of the limited business moved by this niche market, and due to many such languages being maintained by programmers who are computer musicians, this community has been suffering probably more than others from the lack of a systematic, durable approach to the development and maintenance of sound software [3]
We expect this paper—which is not a research paper, and substantially extends material that was presented at a national computer music conference [12]—to be of interest for computer musicians who are planning to import e.g., a new machine learning-based parametric control strategy in their preferred sound processing algorithms, and in general for Python programmers wishing to enrich their background in real-time software
Summary
The development of computer music applications has been qualified as “the expression of compositional or signal processing ideas” [1]. Thanks to a fast learning curve, its rapid prototyping features and intuitive readability of the code, the Python community includes users paying particular attention to the interaction aspects of their software, such as academics [5,6] employing Python as a teaching-by-examples tool [7] Despite this quest for interactivity, the use of Python in real-time applications is testified by exceptions: RTGraph, for instance, instantaneously processes physiological signals and displays the results through the Qt framework [8]. We expect this paper—which is not a research paper, and substantially extends material that was presented at a national computer music conference [12]—to be of interest for computer musicians who are planning to import e.g., a new machine learning-based parametric control strategy in their preferred sound processing algorithms, and in general for Python programmers wishing to enrich their background in real-time software
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