This is an abridged syllabus. You can access the complete syllabus in your Canvas course.

525.655.3VL - Audio Signal Processing

Electrical and Computer Engineering

Fall 2024

Description

This course gives a foundation in current audio and speech technologies, and covers techniques for sound processing by processing and pattern recognition, acoustics, auditory perception, speech production and synthesis, speech estimation. The course will explore applications of speech and audio processing in human computer interfaces such as speech recognition, speaker identification, coding schemes (e.g. MP3), music analysis, noise reduction. Students should have knowledge of Fourier analysis and signal processing.

Instructor

John Carmody

carmody@erols.com

This course contains content produced by faculty members other than the listed instructors including: Dr. Mounya Elhilali.

Course Structure

This is described within the course topics below under Course Topics. Additionally, specific papers will be posted for enrichment, but are not a formal part of the course.

Course Topics

Module #	Module Working Title	Module Description	Assessment	Course Learning Objective Alignment (#)
1	Introduction of audio signal processing	General overview of topic of audio signal processing	Introduction of term project topics and formation of student teams	1
2	Introduction to sound signals	Sound signals and their behavior in a medium, and their physical and perceptual properties.	Quiz1 covers module 2 Discussion on module 2	1,6
3	Signal processing fundamentals	Review of signal processing concepts (both in continuous and discrete-time) as well as the sampling theorem and discrete-time filters.	Quiz2 covers module 3 Discussion on module 3	4
4	Speech production	topics related to the speech production system (i.e. the process of uttering articulated sounds or words) and details of source-filter production models	Quiz3 covers module 4 Discussion on module 4	2,5
5	Sound perception	the hearing system (from the ear to the central auditory pathway) and theoretical formulations of processing in the brain.	Quiz4 covers module 5 Deliverable #1 for term project is due Discussion on module 5	2,3
6	Audio analysis – LPC and cepstral analysis	representations of speech and other audio signals, including methods inspired by speech production models (such as linear predictive coding and cepstral analysis) as well as speech perception models (such as mel-frequency cepstrum)	Discussion on module 6	2,4,5
7	Audio analysis – Time and Frequency domain	representations of audio signals along time and frequency domains	Quiz5 covers modules 6 and 7 Programming assignment 1 covers modules 2-7 Discussion on module 7	2,4,5
8	Audiology	Topics related to audiology and hearing impairment.	Discussion on module 8	3
9	Audio compression	Lossy and lossless compression schemes of audio signals	Quiz6 covers modules 8 and 9 Discussion on module 9	6,7
10	Speech enhancement	Enhancement of speech signals with a focus on spectral-based methods	Discussion on module 10	6,7
11	Auditory scene analysis	Sound separation and perceptual principles underlying the perception of sound mixtures.	Quiz7 covers modules 10, 11 Programming assignment 2 covers modules 6-11 Discussion on module 11	6,7
12	Speech synthesis	Techniques used in text-to-speech technologies.	Deliverable #2 for term project is due Discussion on module 12	6,7
13	Emotion recognition	Affective sound processing and emotion recognition	Discussion on module 13	6,7
14	Spatial audio	Representations of sound in 3D space	Quiz8 covers modules 12, 13 and 14 Term project covers modules 6-14 Final deliverable for term project is due. Discussion on module 14	6,7

Course Goals

This course seeks to enable the student take a specific problem, clearly articulate its goals and objective, identify the success criteria [if appropriate], break the problem up into definable components to provide an approach to solution, identify any risk factors, and develop a schedule to complete the solution according to a fixed schedule.

The course goals also includes providing the student with the ability to identify the most appropriate tools and techniques to solve that problem and which tools are not appropriate.

Lastly, the course will help improve the student's communications skills, conveying details, results, and lessons learned from their project.

Course Learning Outcomes (CLOs)

Define and explain sound signals and their physical and perceptual properties
Describe how speech sounds are produced and how they are processed by the brain
Discuss concepts and techniques related to audiology and hearing impairment
Apply concepts of signals and systems in continuous and discrete-time to sound applications
Analyze speech and other audio signals using time-domain and frequency-domain representations
Evaluate solutions currently used in audio technologies
Create algorithms to process realistic complex audio signals such as speech, music, sounds from nature, and sounds often associated with medical diagnoses.

Textbooks

The text is "Theory and Application of Digital Speech Processing," by Larry Rabiner and Ron Schafer. ISBN-13: 978-0136034285

The book is available as an eBook also.

A supplementary text is "Audio Coding: Theory and Application" by Yuli You; ISBN: 978-1-4419-1753-9; this is also available as an eBook

Other Materials & Online Resources

A variety of preprints, seminal papers, tutorials, and research papers will be posted on Canvas for the students.

Required Software

It is suggested the student know how to leverage Matlab or Python. The use of the freeware program, Audacity may be quite helpful.

Student Coursework Requirements

Graded quizzes (20% of total grade): The quizzes are given roughly every other week and will assess the students’ comprehension and achievement of the learning objectives. Each quiz covers roughly two modules.
Research topic discussions (15% of total grade): Individual students will identify a recent research publication (journal paper or conference proceeding) on a topic relevant to audio signal processing; and will lead a discussion on the topic covered in the paper. The discussion can be led by individual students or as teams. Students will sign up for different weeks throughout the semester.
Two programming assignments (30% of total grade, divided equally): Each assignment is assigned over a period of ~4 weeks. The assignments are hands-on project to replicate, valuate or create a solution to a specific audio processing problem.

One major group project (35% of the total grade): The group project is started in the beginning of the course and last throughout the whole course (to be submitted at the end). Students group in teams and will choose a topic (within stated guidelines of the project). Each team will do their research on the specific topic chosen, review current state of the art on that topic, create their own strategy to tackle the problem.

Grading Policy

The course shall strictly adhere to the JHU standard set of grading guidelines.

Course Policies

The students in this class are all working professionals. The instructor understands that there may be times when time constraints may arise. The students are encouraged to notify the instructor as soon as possible so fair adjustments may be considered.

The quizzes are individual.

Collaboration is highly encouraged for papers and projects, however. We are all professionals so submissions should meet or exceed the commensurate quality for professionals. The students should include any citations used. The final paper must in IEEE, two-column format.

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.

If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.