Audio Signal Processing for Music Applications

Xavier Serra’s course examines music audio through signal-processing models. Fourier analysis leads into time-frequency methods, sinusoidal and harmonic representations, transformations and sound description. Python exercises and open software make this a technical course about analyzing and manipulating sound, with a different emphasis from learning faders and plugins in a DAW.

What you will learn

• Analyze audio with Fourier and short-time methods
• Implement spectral models and sound transformations
• Use Python tools to describe and synthesize sound

Who this suits

The provider lists Intermediate and indicates related experience is required. Mathematical reasoning and willingness to program are important, even though introductory lessons review basic mathematics and Python before the more substantial exercises.

Before you enroll

The workload is about 49 hours and includes programming assignments. This is a demanding technical option within music production, not an accessible first course for someone seeking only practical mixing instruction. Demonstrations use Python under Ubuntu; verify the current setup and package compatibility before starting. Openly licensed teaching material does not mean every audio file found online can be reused without checking its own license.

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Put it into practice

Analyze a short sound you recorded, inspect its spectrum and explain the effect of one analysis parameter. Apply a simple transformation, listen to the result and connect the audible change with the mathematical representation.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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