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Title: Comparison of supervector and majority voting in acoustic scene identification
Authors: Jiang, Y 
Leung, FH 
Issue Date: Nov-2018
Source: 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, China, 19-21 Nov. 2018, p. 1-5
Abstract: Acoustic scene identification aims to identify the acoustic environment from the acoustic signal. Usually one first divides a piece of acoustic signal into multiple short-time frames and then calculates frame-level features. A natural question is then how to make use of these frame-level features for identification purposes. In this paper, we compare two feature aggregation methods. One method is Majority Voting (MV), which treats each frame-level feature as an independent feature vector and then perform identification using majority voting strategies. In this way, an acoustic signal is represented by multiple feature vectors. The other method is Supervector, which maps the frame-level features to a single feature vector. In this way, an acoustic signal is represented by one feature vector. Particularly, we consider three types of Supervector, which are Gaussian Supervector, Factor Analysis Supervector, and i-vector. We then compare Supervector with MV in an acoustic identification task. Different classifiers are employed, including Gaussian Mixture Model (GMM), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Deep Neural Network (DNN). Experimental results indicate that these two feature aggregation methods give very similar performances, nonetheless, each has its own advantages and disadvantages.
Keywords: Acoustic scene identification
Majority voting
Gaussian supervector
Factor analysis supervector
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-5386-6811-5 (Electronic)
978-1-5386-6810-8 (USB)
978-1-5386-6812-2 (Print on Demand(PoD))
DOI: 10.1109/ICDSP.2018.8631624
Rights: © 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.
The following publication Y. Jiang and F. H. F. Leung, "Comparison of Supervector and Majority Voting in Acoustic Scene Identification," 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, 2018, pp. 1-5 is available at
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