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.
The auditory system constantly monitors the environment to protect us from harmful events such as collisions with approaching objects. Auditory looming bias is an astoundingly fast perceptual bias favoring approaching compared to receding auditory motion and was demonstrated behaviorally even in infants of four months in age. The role of learning in developing this perceptual bias and its underlying mechanisms are yet to be investigated. Supervised learning and statistical learning are the two distinct mechanisms enabling neural plasticity. In the auditory system, statistical learning refers to the implicit ability to extract and represent regularities, such as frequently occurring sound patterns or frequent acoustic transitions, with or without attention while supervised learning refers to the ability to attentively encode auditory events based on explicit feedback. It is currently unclear how these two mechanisms are involved in learning auditory spatial cues at different stages of life. While newborns already possess basic skills of spatial hearing, adults are still able to adapt to changing circumstances such as modifications of spectral-shape cues. Spectral-shape cues are naturally induced when the complex geometry especially of the human pinna shapes the spectrum of an incoming sound depending on its source location. Auditory stimuli lacking familiarized spectral-shape cues are often perceived to originate from inside the head instead of perceiving them as naturally external sound sources. Changes in the salience or familiarity of spectral-shape cues can thus be used to elicit auditory looming bias. The importance of spectral-shape cues for both auditory looming bias and auditory plasticity makes it ideal for studying them together.
Born2Hear will combine auditory psychophysics and neurophysiological measures in order to 1) identify auditory cognitive subsystems underlying auditory looming bias, 2) investigate principle cortical mechanisms for statistical and supervised learning of auditory spatial cues, and 3) reveal cognitive and neural mechanisms of auditory plasticity across the human lifespan. These general research questions will be addressed within three studies. Study 1 will investigate the differences in the bottom-up processing of different spatial cues and the top-down attention effects on auditory looming bias by analyzing functional interactions between brain regions in young adults and then test in newborns whether these functional interactions are innate. Study 2 will investigate the cognitive and neural mechanisms of supervised learning of spectral-shape cues in young and older adults based on an individualized perceptual training on sound source localization. Study 3 will focus on the cognitive and neural mechanisms of statistical learning of spectral-shape cues in infants as well as young and older adults.
Project investigator (PI): Robert Baumgartner
Project partner / Co-PI: Brigitta Tóth, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
Supported by Austrian Science Fund (FWF, I 4294-B) and NKFIH.
Computational Hearing and Psychoacoustics investigates several areas which rely on human hearing:
Publications: W. Deutsch