Program Overview

Course Description

The course EQ2321, Speech and Audio Processing, is a 7.5-credit course that considers the foundational and advanced signal and information processing methods for human speech and natural audio signal applications.

Course Contents

The course covers the following topics:

• Preliminaries of associated digital signal processing methodologies, such as convolution, Z-transform, Fourier transform, power spectrum, etc.
• A source-filter model: analysis-synthesis architecture.
• Source coding: scalar and vector quantization, redundancy removal, linear prediction, open loop and closed loop coding, coding noise buildup, coding noise shaping, coding gain.
• Speech and audio coding: vocoders, low bit rate and high bit rate codecs, perceptual audio coding, psychoacoustic principles.
• Speech and audio signal enhancement, minimum mean square error estimation, linear estimation for Gaussian distribution, Wiener filtering, power spectral subtraction methods, spectral band replication, etc.

Intended Learning Outcomes

After passing the course, students should be able to:

1. Qualitatively describe the mechanisms of human speech production and how the articulation mode of different classes of speech sounds determines their acoustic characteristics.
2. Apply programming tools (such as Matlab or Python) to analyze speech and audio signals in time and frequency domains, and in terms of the parameters of a source-filter production model and harmonic models.
3. Critically analyze, compare and implement methods and systems for coding of speech and audio signals, and finally engineer efficient coding solutions.
4. Analyze, compare and implement methods and systems for enhancement of speech and audio signals in environmental noisy conditions.

Literature and Preparations

Specific Prerequisites

For single course students: 120 credits and documented proficiency in English B or equivalent.

Recommended Prerequisites

Recommended prerequisite: EQ1220 Signal Theory or EQ1270 Signal Processing.

Literature

The literature for the course will be announced on the course homepage before the course start. Preliminary literature includes:

1. Digital speech transmission: Enhancement, coding and error concealment. By Peter Vary and Rainer Martin.
2. Perceptual coding of digital audio. By Ted Painter and Andreas Spanias.
3. Notes of the class teacher. This can be downloaded from the course website.
4. Some research papers.

Examination and Completion

Grading Scale

A, B, C, D, E, FX, F.

Examination

The course has three assessment components:

1. Master tests: There will be two master tests in the span of teaching 14 classes. Each test is of 20-30 minutes.
2. Projects: There are two projects. Projects are examined via presentations. Projects can be performed in groups of two persons.
3. Written exam: There is a final written exam.

The overall grade of the course is based on collective performance. The teacher will provide weights to all tests for the overall grade.

To pass the course, master tests are not mandatory. But the projects and final test are mandatory. To achieve a good course grade, a student is expected to perform well in all the three assessment components.

Further Information

Course Room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas.

Offered by

EECS/Intelligent Systems.

Main Field of Study

Electrical Engineering.

Education Cycle

Second cycle.

Supplementary Information

In this course, the EECS code of honor applies.

Information per Course Offering

Termin

Spring 2026.

Information for Spring 2026 Start 13 Jan 2026 Programme Students

• Course location: KTH Campus.
• Duration: 13 Jan 2026 13 Mar 2026.
• Periods: Spring 2026: P3 (7.5 hp).
• Pace of study: 50%.
• Application code: 60460.
• Form of study: Normal Daytime.
• Language of instruction: English.
• Course memo: Course memo is not published.
• Number of places: Min: 10.
• Target group: See connected programs. Open to all programmes as long as it can be included in your programme.
• Planned modular schedule: No information inserted.
• Schedule: Link to schedule.
• Part of programme:
  • Master's Programme, ICT Innovation, year 1, AUSY.
  • Master's Programme, ICT Innovation, year 1, VCCN.
  • Master's Programme, Information and Network Engineering, year 1, MMB, Mandatory.
  • Master's Programme, ICT Innovation, year 1, AUSM.
  • Master's Programme, Systems, Control and Robotics, year 2, RASM.
  • Master's Programme, Systems, Control and Robotics, year 2.
  • Master's Programme, Information and Network Engineering, year 1.

Contact

• Examiner: Saikat Chatterjee.
• Course coordinator: Saikat Chatterjee.
• Teachers: Saikat Chatterjee.