Analysis and Synthesis of Audio Signals

Course Description

Analysis and synthesis of audio signals is significant in all aspects of modern communication audio systems and audio technologies. Therefore it is necessary to know: Detection and classification of acoustic audio signals. Time and frequency presentation of audio signals and audio systems. Theory and application of frequency analysis (MLSSA, TDS, ETC, etc.). Autocorrelation function and acoustic audio spectrum. Filters and filtering techniques. Rapid Fourier transform and filter analysis of transient audio signals. Cross spectrum. Coherence. Frequency response functions. Correlation functions. Impulse response functions. Kepstral analysis. Impact of space parameters on impulse response in acoustic audio technologies. Sound field visualization. Synthesis of acoustic audio.

Learning Outcomes

  1. analyze different types of audio signals
  2. analyze the electroacoustic system using the input and output audio signal
  3. apply different filters and filtering techniques
  4. apply the coherence of the audio signal
  5. apply the frequency response function
  6. analyze the space parameter impact on impulse response in acoustic technologies
  7. describe the visualization of sound field
  8. evaluate the synthesis of audio signals

Forms of Teaching


Theoretical foundations of audio signal analysis and synthesis are discussed. The theoretical part is supplemented by demonstrations, when possible.

Seminars and workshops

Students will write a seminar in the field of audio signal analysis and synthesis.


Students will solve mathematical tasks of analysis and synthesis of audio signals.


Students will analyze the elements of audio signal analysis and synthesis through laboratory exercises in small groups.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 0 % 15 % 0 % 15 %
Seminar/Project 0 % 15 % 0 % 15 %
Mid Term Exam: Written 0 % 20 % 0 %
Final Exam: Written 0 % 20 %
Final Exam: Oral 30 %
Exam: Written 0 % 40 %
Exam: Oral 30 %

Week by Week Schedule

  1. Probability density function; Frequency spectra; Rate of change
  2. Use of transformations in extraction of audio signals features, Use of signal processing in extraction of audio signals features
  3. Analysis and extraction of audio signals features, Musical signal in the time and frequency domain
  4. Acoustical systems with digital waveguide architecture; Acoustical tube; Vibrating string, Border conditions; Nonlinear parameters
  5. Model of string instruments; Piano, Model of wind instruments
  6. Model of electronic instrument; Electric guitar; Modelling with FIR filters; Nonlinear distortion, Additive synthesis; Subtractive synthesis; FM synthesis; Granular synthesis
  7. Acoustic modelling of sound sources (physical modelling synthesis), Hidden Markov Model for speech recognition
  8. Midterm exam
  9. Gaussian Mixture Model for speech recognition, Acoustical and lexical models
  10. Training procedures, Parametric models; Parameter estimation
  11. Speech feature vectors, Cepstral analysis
  12. Hommomorphic analysis, Diphonic
  13. Threephonic, Normalization and modification
  14. Estimation of speech intelligibility and speech quality, Diagnostics and rehabilitation of speech disorders
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Elective Courses of the Profile (2. semester)
Communication and Space Technologies (profile)
Free Elective Courses (2. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (2. semester)
Computer Engineering (profile)
Free Elective Courses (2. semester)
Computer Science (profile)
Free Elective Courses (2. semester)
Control Systems and Robotics (profile)
Free Elective Courses (2. semester)
Data Science (profile)
Free Elective Courses (2. semester)
Electrical Power Engineering (profile)
Free Elective Courses (2. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (2. semester)
Electronic and Computer Engineering (profile)
Free Elective Courses (2. semester)
Electronics (profile)
Free Elective Courses (2. semester)
Information and Communication Engineering (profile)
Free Elective Courses (2. semester)
Network Science (profile)
Free Elective Courses (2. semester)
Software Engineering and Information Systems (profile)
Free Elective Courses (2. semester)


(.), Frequency analysis,
Mark Kahrs, Karlheinz Brandenburg (2006.), Applications of Digital Signal Processing to Audio and Acoustics, Springer Science & Business Media
Saeed V. Vaseghi (2008.), Advanced Digital Signal Processing and Noise Reduction, John Wiley & Sons
G. Longo, B. Picinbono (2014.), Time and Frequency Representation of Signals and Systems, Springer
Jens Trampe Broch (1981.), Principles of Analog and Digital Frequency Analysis,

For students


ID 222457
  Summer semester
L3 English Level
L3 e-Learning
30 Lectures
15 Exercises
10 Laboratory exercises

Grading System

Very Good