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Included in the audiomath package, ready-compiled for common platforms:Įnables sounds to be decoded from common compressed audio and video file formats

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Required for controlling the system volume on Windows In outlining these tasks, we have underlined the recurring lower-level functions that these tasks require:Īutomatically installed alongside audiomath on all platforms:Īutomatically installed alongside audiomath on Windows:

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We identified the need for software tools that facilitate-and, to the greatest extent possible, enable us to automate-the four main tasks enumerated below with minimal effort on the part of the programmer.

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Python was chosen as the basis for this platform due to the maturity and modularity of the language, the power and minimalism of its syntax, the comprehensiveness of both its standard library and the range of high-quality packages available for free from third parties, and its consequent high prevalence in scientific communities.

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Providing full documentation and support to external users is part of our strategy to ensure that modules such as Shady and audiomath remain usable and future-proof as the platform evolves. Another published component, Shady, allows rendering and real-time manipulation of research-quality visual stimuli even on sub-optimal hardware. This is one component of our broader vision of a “scalable neurological assessment platform” (SNAP) which consists of multiple reusable software modules. We created audiomath as part of the Burke-Blythedale Pediatric Neuroscience Research Collaboration, to support field applications of neurotechnology-specifically, EEG-based cognitive assessments in children with brain injuries, performed at the bedside. Its development was motivated by the need for tools for designing and presenting auditory stimuli in neuroscientific research, so we will describe audiomath from the neuroscientist's perspective. Since these functions are integral to a wide variety of use cases in science, engineering, art and entertainment, audiomath's potential applications are many and diverse. The open-source software library audiomath makes it easy for Python programmers to synthesize, record, manipulate, edit, visualize or play sound waveforms.

We report the ways in which audiomath can help to map this territory and provide a simplified path toward each application's particular priority.īy unifying audio-related functionality and providing specialized diagnostic tools, audiomath both simplifies and potentiates the development of neuroscientific applications in Python.

In particular, we highlight the potential conflict between demands for low latency, low variability in latency (“jitter”), cooperativeness, and robustness. We then report audio latency measurements across a range of hardware, operating systems and settings, to illustrate the ways in which hardware and software factors interact to affect stimulus presentation performance, and the resulting pitfalls for the programmer and experimenter. We provide a primer on these problems and the possible approaches to them. The second purpose is to relay what we have learned, during development and application of the software, about the twin challenges of delivering stimuli precisely at a certain time, and of precisely measuring the time at which stimuli were delivered. It also incorporates specialized tools for measuring and optimizing stimulus timing. It minimizes programming effort by providing a simple object-oriented interface that unifies functionality for audio generation, manipulation, visualization, decoding, encoding, recording, and playback. The first is to present audiomath ( ), a sound software library for Python that prioritizes the needs of neuroscientists. In particular, verifying stimulus timing requires extensive engineering effort when developing new applications.

Numerous programming tools exist to approach these tasks, but their differing specializations and conventions demand extra time and effort for integration. In neuroscientific experiments and applications, working with auditory stimuli demands software tools for generation and acquisition of raw audio, for composition and tailoring of that material into finished stimuli, for precisely timed presentation of the stimuli, and for experimental session recording.