A Novel Audio Event Detection Method for Internet of Things

Recently, the Internet of Things （loT） has attracted more and more attention. Multimedia sensor network plays an important role in the IoT, and audio event detection in the multimedia sensor net- works is one of the most important applications for the Internet of Things. In practice, it is hard to get enough real-world samples to generate the classifi- ers for some special audio events （e.g., car-crash- ing in the smart traffic system）. In this paper, we introduce a TrAdaBoost-based method to solve the above problem. By using the proposed approach, we can train a strong classifier by using only a tiny amount of real-world data and a large number of more easily collected samples （e.g., collected from TV programs）, even when the real-world data is not sufficient to train a model alone. We deploy this ap- proach in a smart traffic system to evaluate its per- formance, and the experiment evaluations demonstrate that our method can achieve satisfying results.

Audio Highlight Detection Method for Cloud-Based Multimedia Service System

With the development of cloud-based data centers and multimedia technologies, cloud-based multimedia service systems have been paid more and more attention. Audio highlights detection plays an important role in the cloud-based multimedia service system. In this paper, we proposed a novel highlight detection method to extract the audio highlight effects for the cloud-based multimedia service system using the unsupervised approach. In the proposed method, we first extract the audio features for each audio document. Then the spectral clustering scheme was used to decompose the audio document into several audio effects. Then, we introduce the TF-IDF method to label the highlight effect. We design some experiments to evaluate the performance of the proposed method, and the experimental results show that our method can achieve satisfying results.

A rapid audio event detection method by adopting 2D-Haar acoustic super feature vector

For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an ＂acoustic feature image＂, secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.