Digital Signal Processing 3: Analog versus Digital¶

Course Overview¶

• Institution: EPFL
• Course code: DSP 3
• Track: Digital Signal Processing
• Tier: A
• Role: Alternative
• Level: Not standardized by provider (use prerequisites)
• Last reviewed: 2026-07-28

EPFL's Digital Signal Processing 3: Analog versus Digital compares analog and digital processing from first principles through videos, exercises, labs, and code, recommending basic calculus and linear algebra while potentially requiring paid access.

Why choose this course

Alternative course. A reliable option that can serve as a main course or strong alternative.

Before you start

• Recommended foundation: Signals and Systems
• Recommended foundation: Probability, Statistics, and Random Processes
• Recommended foundation: Programming and Engineering Computing
• Recommended background: Basic calculus and linear algebra; following the specialization numbering is recommended, not required

Verifiable learning outcomes

• Explain the core models in Digital Signal Processing, including their assumptions and limits
• Solve representative derivations and problems, checking units, limiting cases, or numerical results
• Complete a reproducible experiment or implementation with raw data, parameters, versions, and verification

Workload and pacing

11 weeks at 9 hours/week. This maintainer planning estimate is derived from course role and the density of public practice and labs; it is not a provider workload promise. Pilot two weeks while logging instruction, practice, lab, and review time, then adjust the remaining plan when actual effort differs by more than 25%.

Safety level

Simulation only. The default practice scope is software, computation, or simulation only; a lab label in the resource inventory does not authorize connecting physical equipment, and any hardware extension requires provider-scope verification and a new risk assessment.

Course Resources¶

Software, hardware, and cost

Software

• Maintainer-suggested open-source/free verification path: Python 3, Jupyter, NumPy, SciPy, Matplotlib, and GNU Octave
• The resource inventory lists public code coverage; pin interpreter, dependencies, toolchain, datasets, and PDK versions where applicable

Hardware

• The resource inventory lists lab coverage, but this course's maintainer path explicitly limits it to computational or simulation work. It assumes only a general-purpose computer able to run the software above and retain results; do not purchase or connect a course-specified audio interface, DSP/microcontroller board, or software-defined radio; validate with recorded data first

Cost note

The current maintainer path uses computation and simulation only, with no dedicated hardware purchase, and prefers open-source/free tools. This is not a provider requirement; platform, commercial-software, or cloud-compute costs still vary by provider, region, and plan.

Public resource coverage

Resources and access

“Listed by official page” means the link was discovered on a successfully fetched official source on the verification date; it does not guarantee that every region or account can open the target directly. Access does not grant redistribution rights. Re-check the provider page, target link, and third-party notices before downloading, adapting, or publishing material.

Practice and Verification¶

Practice loop

Digital Signal Processing 3: Analog versus Digital · EPFL DSP 3: Filter Implementation and Fixed-Point Error Benchmark

This is a maintainer-suggested self-study project for Digital Signal Processing 3: Analog versus Digital · EPFL DSP 3, not an official course assignment. Design and implement a digital filtering or spectral-estimation pipeline for Digital Signal Processing, comparing floating-point, fixed-point, and reference-library amplitude, phase, noise, and execution cost.

Origin: Maintainer-suggested project

Deliverables

• Measurable specifications for sample rate, pass and stop bands, latency, width, and overflow policy
• Floating reference, from-scratch implementation, fixed-point implementation, and automated tests
• Raw outputs, frequency responses, errors, and runtimes for impulse, sweep, and noise inputs
• A report comparing specifications, quantization noise, overflow, and boundary effects

Verification

• Keep floating-point normalized RMSE against the reference library below 1e-8
• Meet predeclared fixed-point passband ripple and stopband attenuation with no undetected overflow
• Cover DC, Nyquist, full scale, all-zero, and shortest-record boundaries
• Reduce word length stepwise and report the first specification failure and SNR or runtime curve

Reproducibility

• Commit design, implementation, test, signal-generation, and plotting sources
• Pin dependencies, widths, rounding and saturation modes, sample rate, and random seeds
• Preserve raw waveforms, coefficients, performance logs, and the generated report

Safety boundary: Simulation only — Process synthetic or public signals only; do not use the exercise filter for medical monitoring, protection relays, flight control, or other safety-critical decisions.

Risks, gaps, and boundaries

The provider recommends basic calculus and linear algebra but does not list the earlier sequence as a hard prerequisite; full Coursera access may require payment.

Completion evidence

• Weekly learning log with time, questions, corrected errors, decisions, next steps, and links to that week's reproducible artifacts
• Simulation package with model or netlist, inputs, solver and version, parameter-sweep script, benchmark comparison, expected results, and one rerun command
• Code repository with pinned dependencies and toolchain, a minimal run command, tests or waveform/benchmark checks, expected output, and license notes