Audio Processing Laboratory, Summer Term 2020

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Objectives and Requirements

The objective of this lab course is to give students a hands on experience in audio processing. In particular, functions, transforms, and algorithms that are important for analyzing and processing audio signals are covered. The lab course is supervised by members of the AudioLabs team. Requirements are a solid mathematical background, a good understanding of fundamentals in digital signal processing, as well as a general background and personal interest in audio. Furthermore, experience with PYTHON and NUMPY is required. Experience with the R Programming language is beneficial (beginners tutorial is provided in the course material).

Enrollment

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Registration via StudOn is required for this lab. Registration is open from 30.03. - 05.04.20. For an overview of this course, click here. For questions, please contact Michael Krause.

Schedule

Important notes:
Due to the SARS-CoV-2 pandemic, the Audio Processing Laboratory will be offered as a fully virtual course this semester. As a consequence, all students who want to participate must have access to a computer capable of
  • running the ZOOM video conferencing software, including audio and video transmission as well as screensharing.
  • running a regular web browser to access our Python development environment.

The lab consists of one introduction session (2 hours) and five lab units (30 minutes test sessions per unit).

The introductory meeting will take place on Friday, April 24 2020, 16:00 via a video conferencing tool. Links to the meeting will be provided via StudOn and mail.

Students will work on the lab units from home. Questions can be asked via mail and a StudOn forum. Students will then be examined in pairs via video conferencing, including screen sharing. The solutions must be ready for the examination. These examinations will take place on Thursdays 12:15 - 16:15 and Fridays 14:00 - 18:00. Students will be able to sign up via Doodle for a 30 minute slot among these times (assigned on a first-come-first-serve basis).

All participating students have received their login information now. Please contact Michael Krause if you do not have your login information yet!

Check https://www.audiolabs-erlangen.de/fau for the tutors' contact information!

The five lab units will be

  • Lab 1: Short-Time Fourier Transform and Chroma Features

    Homework submission: 05.05.2020, 12pm
    07.05.2020: 12:15 - 16:15
    08.05.2020: 14:00 - 18:00
    Supervisors: Sebastian Rosenzweig, Michael Krause
    Instructions (HTML Export)

  • Lab 2: Virtual Acoustics (Fast Convolution)

    Homework submission: 12.05.2020, 12pm
    14.05.2020: 12:15 - 16:15
    15.05.2020: 14:00 - 18:00
    Supervisors: Carlotta Anemüller, Niklas Winter
    Instructions (HTML Export)

  • Lab 3: Speech Enhancement Using Microphone Arrays

    Homework submission: 02.06.2020, 12pm
    04.06.2020: 12:15 - 16:15
    05.06.2020: 14:00 - 18:00
    Supervisors: Adrian Herzog, Wolfgang Mack
    Instructions (HTML Export)

  • Lab 4: Statistical Methods for Audio Experiments

    Homework submission: 16.06.2020, 12pm
    18.06.2020: 12:15 - 16:15
    19.06.2020: 14:00 - 18:00
    Supervisors: Alexander Adami, Pablo Delgado
    Instructions (PDF)

  • Lab 5: Speech Analysis

    Homework submission: 30.06.2020, 12pm
    02.07.2020: 12:15 - 16:15
    03.07.2020: 14:00 - 18:00
    Supervisors: Ning Guo, Martin Strauß
    Instructions (PDF)

  • Attendance is mandatory for all meetings and test sessions.
  • CME students are required to have passed the CME Prep-Course in order to participate in this lab.
  • Homework exercises are mandatory and need to be done before starting with the lab exercises in written form on paper. Solutions will be submitted via StudOn (as scans or photographs) for each unit 2 days in advance before the first test date in that week.

Links

  • Link to notebook server (reachable from university network or via VPN, logins will be distributed in the introductory meeting)
  • An introduction to Python and Jupyter Notebooks: Link
  • Python docs: Link
  • Jupyter Notebook docs: Link, Try yourself: Link
  • An introduction to SciPy: Link

Assessment criteria

  • The lab courses are designed to be worked on in groups of 2-3 participants
  • Individual points for each of the groups participants will be assigned by the supervisors (Points: 0=no pass, 1=minimal pass , 2=pass, 3=excellent). To pass the lab course you need to pass all five individual labs by having at least 1 point in all five labs. Altogether at least 7 points.