Abstract
This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods.
Highlights
Surveillance systems have become increasingly ubiquitous in our living environment.These systems have been used in various applications including CCTV in traffic and site monitoring, and navigation
The performance was evaluated on recorded sound-event signals in a low noisy environment at 20 signal-to-noise ratios (SNRs)
The sound-event database had a total of 500 recorded signals containing four event classes: speech (SP), door open (DO), door knocking (DK), and footsteps (FS)
Summary
Surveillance systems have become increasingly ubiquitous in our living environment. These systems have been used in various applications including CCTV in traffic and site monitoring, and navigation. An automatic signal separation and event classification algorithm was proposed to improve the surveillance system. The proposed noisy sound separation and event classification method is based on two approaches (i.e., blind signal separation and sound classification, which are introduced in the sections to follow, respectively). Most algorithms of the SEC methods are conveyed from sound classification approaches such as sparse coding, deep learning, and support vector machine (SVM). These approaches have been exploited to categorize a sound event in both indoor and outdoor scenarios. Separate from the deep learning framework, another SEC approach is support vector machines [31,32], which has been practically presented to solve the classifier problem in various fields.
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