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Acoustic event detection from weakly labeled data using auditory salience

Podwinska, Zuzanna, Sobieraj, Iwona, Fazenda, Bruno M, Davies, William J and Plumbley, Mark D. (2019) Acoustic event detection from weakly labeled data using auditory salience In: 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), 12-17 May 2019, Brighton, UK.

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Acoustic Event Detection (AED) is an important task of machine listening which, in recent years, has been addressed using common machine learning methods like Non-negative Matrix Factorization (NMF) or deep learning. However, most of these approaches do not take into consideration the way that human auditory system detects salient sounds. In this work, we propose a method for AED using weakly labeled data that combines a Non-negative Matrix Factorization model with a salience model based on predictive coding in the form of Kalman filters. We show that models of auditory perception, particularly auditory salience, can be successfully incorporated into existing AED methods and improve their performance on rare event detection. We evaluate the method on the Task2 of DCASE2017 Challenge.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Podwinska, Zuzanna
Fazenda, Bruno M
Davies, William J
Plumbley, Mark
Date : 12 May 2019
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : AED; NMF; Auditory salience; Kalman filter
Related URLs :
Depositing User : Clive Harris
Date Deposited : 04 Mar 2019 08:59
Last Modified : 17 Apr 2019 14:52

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