Summary

This paper surveys how Python and the scientific stack (NumPy, SciPy, Matplotlib) can be used to develop audio signal processing applications, including two audio programming libraries built on SciPy. It also describes practical integration with existing music and DSP environments (SndObj and Pure Data), and demonstrates FFT-based analysis, filter design and visualization workflows.

Key Takeaways

• Use NumPy, SciPy and Matplotlib as a compact, readable platform for common audio DSP tasks and visualization.
• Implement FFT-based spectral analysis and spectrograms for audio/speech feature inspection and debugging.
• Design and apply FIR/IIR digital filters using SciPy's signal tools for typical audio filtering needs.
• Integrate Python-based processing with SndObj and Pure Data to connect prototypes to music composition and real-time workflows.
• Prototype and iterate audio DSP algorithms quickly, enabling reproducible experiments and transitions to production code.

Who Should Read This

Intermediate audio/DSP engineers, researchers, or developers who want to prototype audio and speech processing algorithms in Python and integrate with existing composition or real-time tools.

Still RelevantIntermediate

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