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Audio, Voice and Neural Signal Processing

Code: 45680
Credits: 5
2026/2027
Degree programme Type Course
Telecommunication Engineering OP 2

Contact lecturer

Name :
Waldo Nogueira Vazquez
Email :
waldo.nogueira@uab.cat

Teaching staff

Helen Wolf
Constantino Danilo Dragicevic
Mechthild Meierott

Group languages

You can consult this information at the end of the document.

Prerequisites

Specifically, they should be familiar with:

  • Basics of algebra, calculus, probability and statistics,
  • Basic concepts of signals and systems and information theory.
  • Digital signal processing fundamentals, including sampling, filtering, and spectral analysis.
  • Statistical Signal Processing.
  • Integrated Systems for Digital Signal Processing.
  • Programming skills in MATLAB, Python, or a similar scientific computing environment.

Prior knowledge of speech, audio, hearing, neuroscience, or neural signal processing is not required. All domain-specific concepts necessary for the analysis and processing of speech, audio, and neural signals will be introduced during the course.

Objectives

This is a course on digital processing of audio, speech and neural signals. The course focuses on the study of the main techniques for the analysis, description, synthesis, and processing of speech and neural signals together with applications related to smartphone, hearables, head-mounted devices, hearing aids, cochlear implants and diagnostic devices of hearing.


The aim of this course is for students to develop a methodology to analyze, encode, recognize audio, speech and neural signals using signal processing techniques. More specifically, students are expected to acquire theoretical and practical competencies related to:

  • Acoustic, physiological, and perceptual foundations of speech and hearing.
  • Fundamentals of the digital analysis of speech and neural signals.
  • Methods for auditory system modeling and the processing of speech and neural signals.
  • Use of software tools for speech and neural signal processing.
  • Computational models of the human auditory system
  • Design hearing devices, from heareables to hearing devices to restore hearing loss
  • Design diagnostic devices of hearing
  • Understand and interprete electrophysiological methods to diagnose hearing
  • Implementation, using a programming language, of speech and neural signal processing algorithms.



Learning outcomes

  • (CA39) Investigate the relationship between sound acoustics and neural signals.
  • (CA40) Examine the methodology needed to design and evaluate audio, speech, and neural signal processing algorithms.
  • (CA41) Implement advanced signal processing algorithms for speech, audio, and neural signals in Matlab and Python.
  • (CA42) Apply knowledge in signal processing in bioengineering and more specifically in audiology and hearing technologies.
  • (KA36) Explain the relationship between sound acoustics and neural processing, highlighting its technical implications.
  • (KA37) Describe the physiology of the human auditory system and its computational modelling, integrating concepts of psychoacoustics.
  • (KA38) Design advanced algorithms for audio and neural signal processing.
  • (KA39) Describe signal processing techniques for the recognition, transmission and synthesis of audio and speech.
  • (KA40) List signal processing applications focused on hearing aids, cochlear implants, and audiologic diagnostic systems.
  • (SA52) Analyse signal processing, including filter design, advanced audio, voice, and neural signal modelling.
  • (SA53) Implement signal processing algorithms in Matlab and Python.
  • (SA54) Report orally and in writing on scientific publications in the field of audio, voice and neural signal processing.
  • (SA55) Apply computational mathematical modelling to the auditory system and voice production.

Contents

1. Introduction to Speech, Audio, and Neural Signals

  • Characteristics of speech, audio, and neural signals.
  • Signal acquisition systems and sensors.
  • Fundamentals of speech production, hearing, and neural activity.
  • Applications in telecommunications, hearing technologies, and neuroengineering.

2. Fundamentals of Digital Signal Processing

  • Sampling, quantization, and digital signal representation.
  • Time-domain and frequency-domain analysis.
  • Fourier analysis and spectral estimation.
  • Digital filtering and signal enhancement techniques.

3. Acoustics, Binaural Hearing, and 3D Audio

  • Fundamentals of acoustics.
  • Headphone- and loudspeaker-based reproduction systems.
  • Binaural hearing.

4. Auditory System and Hearing Technologies

  • Anatomy and physiology of the peripheral and central auditory system.
  • Auditory perception and psychoacoustics.
  • Models of cochlear and auditory processing.
  • Hearing loss: causes, diagnosis, and implications for signal processing.
  • Overview of hearing assistive technologies.

5. Speech and Audio Signal Processing for Hearing Applications

  • Speech analysis and feature extraction.
  • Speech enhancement and noise reduction.
  • Directional processing and beamforming techniques.
  • Dynamic range compression and frequency shaping.
  • Speech intelligibility and quality assessment in hearing devices.

6. Hearing Aids: Signal Processing Algorithms

  • Architecture and operation of modern hearing aids.
  • Adaptive filtering and feedback cancellation.
  • Noise reduction and speech enhancement algorithms.
  • Automatic scene classification and environmental adaptation.
  • Wireless connectivity and real-time processing constraints.

7. Auditory Implants

  • Principles of cochlear implant operation.
  • Sound coding strategies for cochlear implants.
  • Signal processing for electric hearing.
  • Bilateral cochlear implant systems and bimodal hearing.
  • Auditory brainstem implants (ABIs) and central auditory implants.
  • Challenges of auditory prostheses and future developments.

8. Computational Models of Hearing

  • Computational models of the acoustically stimulated peripheral auditory system.
  • Computational models of the electrically stimulated peripheral auditory system.
  • Central computational models of loudness, pitch perception, and speech understanding.

9. Auditory Diagnostic Devices

  • Tympanometry.
  • Otoacoustic emissions.
  • Auditory Brainstem Responses (ABR).
  • Auditory Steady-State Responses (ASSR).
  • Cortical auditory evoked potentials.
  • Neural tracking.

10. Neural Signal Processing for Hearing Applications

  • Introduction to EEG, auditory evoked potentials, and auditory steady-state responses.
  • Signal acquisition and preprocessing.
  • Filtering and artifact removal techniques.
  • Time-frequency analysis of neural responses.
  • Neural measures of auditory perception and speech understanding.
  • Applications to objective fitting and evaluation of hearing devices and auditory implants.

11. Machine Learning for Speech, Audio, and Neural Signals

  • Feature extraction and dimensionality reduction.
  • Classification and pattern recognition methods.
  • Machine learning applications in hearing aids and cochlear implants.
  • Neural decoding and brain-computer interfaces.
  • Data-driven approaches for personalized auditory rehabilitation.

12. Laboratory Sessions

  • Speech, audio, and neural signal processing using MATLAB and/or Python.
  • Implementation of algorithms for hearing aids and cochlear implants.
  • Analysis of auditory evoked potentials and neural datasets.
  • Evaluation of speech intelligibility and auditory performance.
  • Auditory diagnostic measurements: tympanometry, otoacoustic emissions, Auditory Brainstem Responses (ABR), Auditory Steady-State Responses (ASSR), and cortical auditory evoked potentials.

Learning activities and methodology

Title Hours ECTS Learning outcomes
Type: Guided
1. Introduction to Speech, Audio, and Neural Signals 6 0.24 CA40
10. Neural Signal Processing for Hearing Applications 6 0.24 KA36, KA38
11. Diangostic devices of hearing 0 0 CA42, KA40
11. Machine Learning for Speech, Audio, and Neural Signals 6 0.24 KA38, KA39
12. Laboratory Sessions 47 1.88 SA53, SA54
2. Fundamentals of Digital Signal Processing 6 0.24 CA41, CA42
3. Acoustics, Binaural Hearing, and 3D Audio 6 0.24 CA39, KA36
4. Auditory System and Hearing Technologies 6 0.24 KA37
5. Speech and Audio Signal Processing for Hearing Applications 6 0.24 CA41, CA42, KA38, KA39, SA52
6. Hearing Aids: Signal Processing Algorithms 6 0.24 CA40, CA42, KA37, KA38, KA40, SA52
7. Auditory Implants 6 0.24 KA40
8. Computational Models of Hearing 6 0.24 KA37, SA52, SA55

L'assignatura combina classes magistrals, sessions de laboratori, estudis de casos i aprenentatge basat en projectes per proporcionar als estudiants una base teòrica sòlida i competències pràctiques en el processament de senyals de parla, àudio i senyals neuronals, amb un èmfasi especial en les tecnologies auditives i els implants auditius.


Classes teòriques

  1. Presentació dels fonaments teòrics del processament digital de senyals, la percepció auditiva, els dispositius auditius i l'anàlisi de senyals neuronals.
  2. Discussió de les tecnologies d'avantguarda en audiòfons, implants coclears, implants auditius de tronc cerebral i implants auditius centrals.
  3. Anàlisi de la literatura científica i dels reptes d'enginyeria en contextos reals.

Sessions de laboratori

  1. Exercicis pràctics utilitzant MATLAB i/o Python.
  2. Processament i anàlisi de senyals de parla, àudio i senyals neuronals.
  3. Disseny i implementació d'algorismes de processament de senyals emprats en audiòfons i implants auditius.
  4. Avaluació d'estratègies de millora de la parla, reducció del soroll i codificació auditiva.
  5. Mesures electrofisiològiques basades en l'electroencefalografia (EEG) en resposta a l'estimulació acústica.


Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Final Exam 40% 2 0.08 CA40, CA41, CA42, KA36, KA37, KA38, KA39, KA40, SA52, SA55
Laboratories 40% 15 0.6 CA39, CA40, CA41, CA42, KA36, KA37, KA38, KA39, KA40, SA52, SA53, SA54, SA55
Midterm Exam 20% 1 0.04 CA39, CA40, CA41, CA42, KA36, KA37, KA39, KA40, SA52

Theory (60%)

  • Midterm examination: 30%
  • Final examination: 30%


Laboratory Sessions (40%)

  • Five laboratory sessions, with mandatory attendance and submission of the corresponding reports: 40%


Minimum Requirements to Pass the Course

  • Students must obtain a minimum grade of 4.0 out of 10 in the overall theory component (midterm examination and final examination combined).
  • Attendance at all laboratory sessions is mandatory. To pass the course, students must participate in all practical activities and submit the corresponding reports within the established deadlines.
  • Absences from laboratory sessions must be duly justified. In such cases, the teaching staff may assign equivalent compensatory activities.


Resit Assessment

The theoretical component of the course is eligible for resit. Students who do not meet the minimum requirements or fail the theory component may sit a resit examination, which may cover the contents of the midterm examination, the final examination, or both, as determined by the teaching staff.

If a student takes the resit assessment, the final course grade will be calculated according to the following weighting:

  • Midterm resit examination: 30%
  • Final resit examination: 30%
  • Laboratory sessions: 40%

Bibliography

Core Signal Processing

  • Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-Time Signal Processing (3rd ed.). Pearson.
  • Proakis, J. G., & Manolakis, D. G. (2007). Digital Signal Processing: Principles, Algorithms, and Applications (4th ed.). Pearson.
  • Rabiner, L. R., & Schafer, R. W. (2011). Theory and Applications of Digital Speech Processing. Pearson.

Speech and Audio Processing

  • Rabiner, L. R., & Schafer, R. W. (2007). Introduction to Digital Speech Processing. Foundations and Trends in Signal Processing, 1(1-2), 1-194. https://doi.org/10.1561/2000000001
  • Jurafsky, D., & Martin, J. H. (2026). Speech and Language Processing (3rd ed., online manuscript). Stanford University.

Hearing Science and Auditory Signal Processing

  • Moore, B. C. J. (2022). An Introduction to the Psychology of Hearing (8th ed.). Brill.
  • Pickles, J. O. (2012). An Introduction to the Physiology of Hearing (4th ed.). Emerald Publishing.

Hearing Aids

  • Dillon, H. (2012). Hearing Aids (2nd ed.). Thieme Medical Publishers. ISBN: 978-1604068108.

Cochlear Implants and Auditory Prostheses

  • Zeng, F.-G., Rebscher, S., Harrison, W., Sun, X., & Feng, H. (2008). Cochlear implants: System design, integration, and evaluation. IEEE Reviews in Biomedical Engineering, 1, 115-142. https://doi.org/10.1109/RBME.2008.2008250
  • Clark, G. M. (2003). Cochlear Implants: Fundamentals and Applications. Springer. ISBN: 978-0387955834.
  • Wolfe, J. (2020). Cochlear Implants: Audiologic Management and Considerations for Implantable Hearing Devices. Plural Publishing. ISBN: 978-1597568920.
  • Nogueira, W. (2026). Advancements and Challenges in Signal Processing and Sound Coding for Cochlear Implants. In Advancing Cochlear Implants. Springer.

Neural Signal Processing and Auditory Electrophysiology

  • Sanei, S., & Chambers, J. A. (2007). EEG Signal Processing. John Wiley & Sons. ISBN: 978-0470025819. https://doi.org/10.1002/9780470511923
  • Atcherson, S. R., & Stoody, T. M. (2024). Auditory Electrophysiology: A Clinical Guide (2nd ed.). Thieme Medical Publishers. ISBN: 978-1684201167.




Software

  • Python
  • Matlab with signal processing toolbox, statistic toolbox, audio toolbox
  • Audacity
  • Praat
  • EEGlab

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE