Research Topics/Groups

This is the companion Webpage of the manuscript:

Audlet Filter Banks: A Versatile Analysis/Synthesis Framework using Auditory Frequency Scales

Thibaud Necciari, Nicki Holighaus, Peter Balazs, Zdeněk Průša, Piotr Majdak, and Olivier Derrien.

Abstract: Many audio applications rely on filter banks (FBs) to analyze, process, and re-synthesize sounds. For these applications, an important property of the analysis-synthesis system is the reconstruction error; it has to be kept to a minimum to avoid audible artifacts. Other advantageous properties include stability and low redundancy. To exploit some aspects of human auditory perception in the signal chain, some applications rely on FBs that approximate the frequency analysis performed in the auditory periphery, the gammatone FB being a popular example. However, current gammatone FBs only allow partial reconstruction and stability at high redundancies. In this article, we construct an analysis-synthesis system for audio applications. The proposed system, named Audlet, is based on an oversampled FB with filters distributed on auditory frequency scales. It allows perfect reconstruction for a wide range of FB settings (e.g., the shape and density of filters), efficient FB design, and adaptable redundancy. In particular, we show how to construct a gammatone FB with perfect reconstruction. Experiments demonstrate performance improvements of the proposed gammatone FB when compared to current gammatone FBs in terms of reconstruction error and stability, especially at low redundancies. An application of the framework to audio source separation illustrates its utility for audio processing.

Sound examples for the source separation experiment: click on a system's acronym to hear the corresponding reconstruction.
Reference signals: original mixture -- target

Rt β = 1 β = 1/6 1024-point STFT
1.1 trev_gfb Audlet_gfb Audlet_hann trev_gfb Audlet_gfb Audlet_hann STFT_hann
1.5 trev_gfb Audlet_gfb Audlet_hann trev_gfb Audlet_gfb Audlet_hann STFT_hann
4.0 trev_gfb Audlet_gfb Audlet_hann trev_gfb Audlet_gfb Audlet_hann STFT_hann

AABBA is an intellectual open group of scientists collaborating on development and applications of models of human spatial hearing

AABBA's goal is to promote exploration and development of binaural and spatial models and their applications.

AABBA members are academic scientists willing to participate in our activities. We meet annually for an open discussion and progress presentation, especially encouraging to bring in students and young scientists associated with members’ projects to our meetings. Our activities consolidate in joint publications and special sessions at international conferences. As a relevant tangible outcome, we provide validated (source) codes for published models of binaural and spatial hearing to our collection of auditory models, known as the auditory modeling toolbox (AMT).

Structure

  • Executive board: Piotr Majdak, Armin Kohlrausch, Ville Pulkki

  • Members:

    • Aachen: Janina Fels, ITA, RWTH Aachen
    • Bochum: Dorothea Kolossa & Jens Blauert, Ruhr-Universität Bochum
    • Cardiff: John Culling, School of Psychology, Cardiff University
    • Copenhagen: Torsten Dau & Tobias May, DTU, Lyngby
    • Dresden: Ercan Altinsoy, TU Dresden
    • Ghent: Sarah Verhulst, Ghent University
    • Guangzhou: Bosun Xie, South China University of Technology, Guangzhou
    • Helsinki: Ville Pulkki & Nelli Salminen, Aalto University
    • Ilmenau: Alexander Raake, TU Ilmenau
    • Kosice: Norbert Kopčo, Safarik University, Košice
    • Lyon: Mathieu Lavandier, Université de Lyon
    • Munich I: Werner Hemmert, TUM München
    • Munich II: Bernhard Seeber, TUM München 
    • Oldenburg: Bernd Meyer, Carl von Ossietzky Universität Oldenburg
    • Oldenburg-Eindhoven: Steven van de Par & Armin Kohlrausch, Universität Oldenburg
    • Patras: John Mourjopoulos, University of Patras
    • Rostock: Sascha Spors, Universität Rostock
    • Sheffield: Guy Brown, The University of Sheffield
    • Tabriz: Masoud Geravanchizadeh, University of Tabriz
    • Toulouse: Patrick Danès, Université de Toulouse
    • Troy: Jonas Braasch, Rensselaer Polytechnic Institute, Troy
    • Vienna: Bernhard Laback & Robert Baumgartner, Austrian Academy of Sciences, Wien
    • The AMT (Umbrella Project): Piotr Majdak
AABBA Group 2019
AABBA group as of the 11th meeting 2019 in Vienna.

Meetings

Annual meetings are held at the beginning of each year:

  • 12th meeting: 16-17 January 2020, Vienna
  • 11th meeting: 19-20 February 2019, Vienna. Schedule.
  • 10th meeting: 30-31 January 2018, Vienna. Schedule. Group photo
  • 9th meeting: 27-28 February 2017, Vienna. Schedule.
  • 8th meeting: 21-22 January 2016, Vienna. Schedule.
  • 7th meeting: 22-23 February 2015, Berlin.
  • 6th meeting: 17-18 February 2014, Berlin.
  • 5th meeting: 24-25 January 2013, Berlin.
  • 4th meeting: 19-20 January 2012, Berlin.
  • 3rd meeting: 13-14 January 2011, Berlin.
  • 2nd meeting: 29-30 September 2009, Bochum.
  • 1st meeting: 23-26 March 2009, Rotterdam.

Activities

  • Upcoming: Special Session "Binaural models: development and applications" at the ICA 2019, Aachen.
  • Special Session "Models and reproducible research" at the Acoustics'17 (EAA/ASA) 2017, Boston.
  • Structured Session "Applied Binaural Signal Processing" at the Forum Acusticum 2014, Krakòw.
  • Structured Session "The Technology of Binaural Listening & Understanding" at the ICA 2016, Buenos Aires.

Contact person: Piotr Majdak

Machine Learning

Machine learning has become an integral part of our everyday lives over the last few year. Whether we use a smartphone, shop online, consume media, drive a car or much more, machine learning (ML) and, more generally, artificial intelligence (AI) support, influence and analyze us in different life situations. In particular deep learning methods based on artificial neural networks are used in many areas.

Also in the sciences ML and AI have already generated important impulses and it is expected that this influence will spread in the future to an even wider field of scientific disciplines.

This increases both the interest in a deeper, science-based understanding of ML methods, as well as the need for scientists of various disciplines to develop a strong understanding of the application and design of such methods.

The Institute for Acoustic Research, which conducts application-oriented basic research in the field of acoustics, is rising to this challenge and as founded the Machine Learning research group.

It sheds light on the different aspects of machine learning and artificial intelligence, with a particular focus on potential applications in acoustics. The collaboration of scientists from different disciplines in the areas of ML and AI will not only enable the Institute for Acoustic Research to make pioneering progress in all areas of sound research, but will also make essential contributions to theoretical issues in the highly up-to-date research field of artificial intelligence.


Staff

Computational Hearing and Psychoacoustics investigates several areas which rely on human hearing:

  • Psychoacoustics (proper): is concerned with the perception of sound in general. Main topics include pitch timbre, loudness and temporal aspects of sounds.
  • Noise Abatement: investigates the acoustic and psychoacoustic description of unwanted sounds and supports the specification of methods for reducing noise, from whatever source (Sound Quality Design).
  • Speech and Language Processing: this area is involved with acoustic aspects of phonetics and linguistics.
  • Comparative and Systematic Musicology: application of psychoacoustic models in the acoustic analysis of music and the human perception thereof.

Contact: This email address is being protected from spambots. You need JavaScript enabled to view it.

Publications: W. Deutsch

Current projects