This course is an introduction to the real-time implementation of digital signal processing (DSP) algorithms, with an emphasis on audio signal processing and audio effffects.
The course will use Matlab and Python programming. Some Matlab experience is expected. No experience in Python required; the course will introduce Python as needed. This course can be taken independently of ECE 6113 and ECE 7133 (DSP I and DSP II).
Topics include: Audio input-output and buffffering. Filtering (recursive and non-recursive fifilters, structures).
Fast Fourier transform and windowed spectral analysis. Digital audio effffects (delay line, amplitude modulation,reverberation, distortion, short-time Fourier transform). Students will learn to implement these algorithms for real-time audio processing in software.
Discrete-Time Signal and Systems (undergraduate level is suffiffifficient) (ECE 3054 or ECE 6113 or equivalent) You should know: discrete-time convolution, Z-transform, transfer function, frequency response, difffference equations, pole-zero diagrams, and the discrete-time Fourier transform.
- Introduction to Python
Binary data and the pack function
Wave fifiles2. The PyAudio library
Graphical user interfaces (GUI) in Matlab
- Real-time fifiltering of microphone signals
The classical recursive fifilters
- Circular buffffers
The vibrato effffect
- Block processing for real-time processing
The Python Matplotlib library
Real-time plotting of audio signals
- The Fast Fourier Transform
The Numpy Library
Graphical user interfaces (GUIs) in Python using the TKinter library
- Keyboard interactivity using TKinter
Simulating a guitar (Karplus-Strong algorithm)
Complex amplitude modulation for voice transformation
- Image and real-time video processing in Python using CV2
Processing audio from two microphones
- The short-time Fourier transform (STFT)
Audio effffects using the STFT
FFT-based convolution (and overlap-add algorithm) for real-time fifiltering
Acoustic impulse response examples
- Reverberation and room impulse responses
Room impulse response measurements using chirps
Chirp signal signals and matched fifiltering
- All-pass systems
Fractional delay systems
- Parametric fifilters
- Multirate Systems
Discrete-cosine transform (DCT)
Principle component analysis (PCA)
ProjectStudents will complete a real-time audio programming project and make a video presentation to be shared with the class.
Grading, Category weights
In the event of academic dishonesty, a score of zero may be given for the item at issue. Additionally, the grade for the course may be reduced, including a failing grade for the course.
Matlab at NYU: https://www.nyu.edu/life/information-technology/computing-support/software/software/matlab.html
Python : http://www.python.org
PyAudio : http://people.csail.mit.edu/hubert/pyaudio/
- The implementation and design of algorithms for signal processing with an emphasis on audio processing.
- Software-based real-time programming of signal processing functions (real-time fifiltering, time-varying fifiltering, spectral analysis, audio effffects).
- Students will be able to use Matlab and Python to perform signal processing functions (fifiltering, spectral analysis, fifilter design).
- Students will understand constraints and parameters associated with real-time signal processing (sampling rate, latency, buffffering, bits per sample).
- Students will be able to write programs to perform audio effffects (reverberation, delay line effffects, amplitude modulation, distortion).
If you are ill or have a personal emergency during the semester
If you are experiencing an illness or other situation that will likely affffect your academic performance in a class,please email Deanna Rayment, Coordinator of Student Advocacy, Compliance and Student Affffairs. Deanna can reach out to your instructors on your behalf when warranted.
The NYU Tandon School values an inclusive and equitable environment for all our students. I hope to foster a sense of community in this class and consider it a place where individuals of all backgrounds, beliefs, ethnicities,national origins, gender identities, sexual orientations, religious and political affiffiffiliations, and abilities will be treated with respect. It is my intent that all students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefifit. If this standard is not being upheld, please feel free to speak with me.
Moses Center Statement of Disability
If you are student with a disability who is requesting accommodations, please contact New York University’s Moses Center for Students with Disabilities (CSD) at 212-998-4980 or email@example.com. You must be registered withCSD to receive accommodations. Information about the Moses Center can be found at http://www.nyu.edu/csd. The Moses Center is located at 726 Broadway on the 3rd flfloor.
NYU School of Engineering Policies and Procedures on Academic Misconduct
Introduction: The School of Engineering encourages academic excellence in an environment that promotes honesty,integrity, and fairness, and students at the School of Engineering are expected to exhibit those qualities in their academic work. It is through the process of submitting their own work and receiving honest feedback on that work that students may progress academically. Any act of academic dishonesty is seen as an attack upon the School and will not be tolerated. Furthermore, those who breach the School’s rules on academic integrity will be sanctioned under this Policy. Students are responsible for familiarizing themselves with the School’s Policy on Academic Misconduct.
Defifinition: Academic dishonesty may include misrepresentation, deception, dishonesty, or any act of falsifification committed by a student to inflfluence a grade or other academic evaluation. Academic dishonesty also includes intentionally damaging the academic work of others or assisting other students in acts of dishonesty. Common examples of academically dishonest behavior include, but are not limited to, the following:
- Cheating: intentionally using or attempting to use unauthorized notes, books, electronic media, or electronic communications in an exam; talking with fellow students or looking at another person’s work during an exam; submitting work prepared in advance for an in-class examination; having someone take an exam fo you or taking an exam for someone else; violating other rules governing the administration of examinations.
- Fabrication: including but not limited to, falsifying experimental data and/or citations.
- Plagiarism: intentionally or knowingly representing the words or ideas of another as one’s own in any academic exercise; failure to attribute direct quotations, paraphrases, or borrowed facts or information.
- Unauthorized collaboration: working together on work that was meant to be done individually.
- Duplicating work: presenting for grading the same work for more than one project or in more than one class, unless express and prior permission has been received from the course instructor(s) or research adviser involved.
- Forgery: altering any academic document, including, but not limited to, academic records, admissions materials, or medical excuses.
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