Discrete-Time Signal Processing¶

Course Overview¶

• Institution: MIT
• Course code: 6.341
• Track: Digital Signal Processing
• Tier: A
• Role: Alternative
• Level: Not standardized by provider (use prerequisites)
• Last reviewed: 2026-07-28

MIT's Discrete-Time Signal Processing offers an alternative discrete-time DSP spine through notes, practice, labs, exams, and code, with its old Athena and MATLAB project workflow needing a Python migration.

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

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 7 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

Discrete-Time Signal Processing · MIT 6.341: Filter Implementation and Fixed-Point Error Benchmark

This is a maintainer-suggested self-study project for Discrete-Time Signal Processing · MIT 6.341, 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 Athena and MATLAB project workflow is old and should be accompanied by a Python migration path.

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