A hybrid approach with multi-channel i-vectors and convolutional neural networks for acoustic scene classification

Autoren Hamid Eghbal-zadeh
Bernhard Lehner
Matthias Dorfer
Gerhard Widmer
Editoren
Titel A hybrid approach with multi-channel i-vectors and convolutional neural networks for acoustic scene classification
Buchtitel Proceedings of the 25th European Signal Processing Conference ( EUSiPCO2017)
Typ in Konferenzband
Verlag IEEE
ISBN 978-0-9928626-7-1
DOI 10.23919/EUSIPCO.2017.8081711
Monat October
Jahr 2017
Seiten 2749-2753
SCCH ID# 17064
Abstract

In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted from monaural audio signals lead to i-vectors that exhibit poor performance, especially on indoor acoustic scenes. At the same time, Convolutional Neural Networks (CNNs) are well known for their ability to learn features by optimizing their filters. They have been applied on ASC and have shown promising results. In this paper, we first propose a novel multi-channel i-vector extraction and scoring scheme for ASC, improving their performance on indoor and outdoor scenes. Second, we propose a CNN architecture that achieves promising ASC results. Further, we show that i-vectors and CNNs capture complementary information from acoustic scenes. Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique. Using our method, we participated in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1st rank among 49 submissions, substantially improving the previous state of the art.