Audio Event Classification Research Articles

Classification of Overlapped Audio Events Based on AT, PLSA, and the Combination of Them

Audio event classification, as an important part of Computational Auditory Scene Analysis, has attracted much attention. Currently, the classification technology is mature enough to classify isolated audio events accurately, but for overlapped audio events, it performs much worse. While in real life, most audio documents would have cer- tain percentage of overlaps, and so the overlap classifica- tion problem is an important part of audio classification. Nowadays, the work on overlapped audio event classifica- tion is still scarce, and most existing overlap classification systems can only recognize one audio event for an overlap. In this paper, in order to deal with overlaps, we innova- tively introduce the author-topic (AT) model which was first proposed for text analysis into audio classification, and innovatively combine it with PLSA (Probabilistic La- tent Semantic Analysis). We propose 4 systems, i.e. AT, PLSA, AT-PLSA and PLSA-AT, to classify overlaps. The 4 proposed systems have the ability to recognize two or more audio events for an overlap. The experimental results show that the 4 systems perform well in classifying overlapped audio events, whether it is the overlap in training set or the overlap out of training set. Also they perform well in clas- sifying isolated audio events.

Application of acoustic directional data for audio event recognition via HMM/CRF in perimeter surveillance systems

Audio event detection (AED) and recognition is a signal processing and analysis domain used in a wide range of applications including surveillance, home automation and behavioral assessment. The field presents numerous challenges to the current state-of-the-art due to its highly nonlinear nature. High false alarm rates (FARs) in such applications particularly limit the capabilities of vision-based perimeter monitoring systems by inducing high operator dependence. On the other hand, conventional fence-based vibration detectors and pressure-driven “taut wires” offer high sensitivity at the cost of a high FAR due to debris, animals and weather.This work reports an audio event identification methodology implemented as a test-bed system for a surveillance application to reduce FAR, maximize blind-spot coverage and improve audio event classification accuracy. The first phase utilizes a nonlinear autoregressive classifier to locate and classify discrete audio events via an exogenous sound direction variable to improve classifier confidence. The second phase implements a time-series-based system to recognize various audio activity groups from nominal everyday sound events such as traffic and muffled speech. The discretely labeled data is thus trained with HMM and Conditional Random Field classifiers and reports a substantial improvement in classification accuracies of indoor human activities.

Audio Event Classification Using SVM with GMM-UBM Supervectors

오디오 이벤트 인식은 신호처리, 오디오 검색, 패턴인식 등의 분야에서 매우 흥미롭고 도전적인 연구주제이다. 본 논문에서는 오디오 이벤트 인식을 위해 UBM(universal background model)에 기반한 GMM 수퍼벡터 및 SVM을 적용하였다. 특징 파라미터로는 MFCC를 사용하였으며, 다양한 커널에 대하여 성능을 검토하였다. GMM 수퍼벡터는 UBM에 대해 MAP 적응하여 얻었으며, 모든 평균 벡터 성분을 연쇄하여 얻었다. 얻어진 수퍼벡터는 SVM 분류기에 입력되어, 최종 인식이 이루어진다. 오디오 이벤트 데이터베이스에 기반한 인식 실험 결과 수퍼벡터 기반 SVM 방법이 GMM-UBM 베이스라인 시스템보다 우수하였다. 더욱이, SVM에 GUMI 커널을 적용한 경우 에러율이 16 mixture에 대하여 26.51%에서 14.9%로 감소하였다.

Normal and Abnormal Non-Speech Audio Event Detection Using MFCC and PR-Based Feature Sets

Non-speech audio event detection and classification has become a very active subject of research, since it can be implemented in many important areas: audio surveillance and context awareness systems. In this study, non-speech normal and abnormal audio events were detected by Mel-frequency cepstrum coefficients (MFCC) and Pitch range (PR) based features using artificial neural network (ANN) classifiers. We have 4 abnormal events (glass breaking, dog barking, scream, gunshot) and 2 normal events (engine noise and rain). Event detection, using ANN classifiers, resulted in an accuracy of up to 92%, with recognition rates overall in the range of 78%-87.5%.