This is the companion Webpage of the manuscript:
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.
|Rt||β = 1||β = 1/6||1024-point STFT|
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).
Executive board: Piotr Majdak, Armin Kohlrausch, Ville Pulkki
Annual meetings are held at the beginning of each year:
Contact person: Piotr Majdak
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.
Computational Hearing and Psychoacoustics investigates several areas which rely on human hearing:
Publications: W. Deutsch