Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
Browse
4 results
Search Results
Item Audio-based Event Detection in Office Live Environments Using Optimized MFCC-SVM Approach(2015) Kucukbay, Selver Ezgi; Sert, Mustafa; 0000-0002-7056-4245; AAB-8673-2019Audio data contains several sounds and is an important source for multimedia applications. One of them is unstructured Environmental Sounds (also referred to as audio events) that have noise-like characteristics with flat spectrums. Therefore, in general, recognition methods applied for music and speech data are not appropriate for the Environmental Sounds. In this paper, we propose an MFCC-SVM based approach that exploits the effect of feature representation and learner optimization tasks for efficient recognition of audio events from audio signals. The proposed approach considers efficient representation of MFCC features using different window and hop sizes by changing the number of Mel coefficients in the analyses as well as optimizing the SVM parameters. Moreover, 16 different audio events from the IEEE Audio and Acoustic Signal Processing (AASP) Challenge Dataset, namely alert, clear throat, cough, door slam, drawer, keyboard, keys, knock, laughter, mouse, page turn, pen drop, phone, printer, speech, and switch that are collected from office live environments are utilized in the evaluations. Our empirical evaluations show that, when the results of the proposed methods are chosen for MFFC feature and SVM classifier, the tests conducted through using 5-fold cross validation gives the results of 62%, 58% and 55% for Precision, Recall and F-measure scores, respectively. Extensive experiments on audio-based event detection using the IEEE AASP Challenge dataset show the effectiveness of the proposed approach.Item Video Scene Classification Using Spatıal Pyramid Based Features(2014) Sert, Mustafa; Ergun, Hilal; https://orcid.org/0000-0002-7056-4245; AAB-8673-2019Recognition of video scenes is a challenging problem due to the unconstrained structure of the video content. Here, we propose a spatial pyramid based method for the recognition of video scenes and explore the effect of parameter optimization to the recognition accuracy. In the experiments different sampling methods, dictionary sizes, kernel methods, and pyramid levels are examined. Support Vector Machine (SVM) is employed for classification due to the success in pattern recognition applications. Our experiments show that, the size of dictionary and proper pyramid levels in feature representation drastically enhance the recognition accuracy.Item Facial Action Unit Detection using Variable Decision Thresholds(2016) Aksoy, Nukhet; Sert, Mustafa; 0000-0002-7056-4245; D-3080-2015Detection of facial action units (AUs) is an important research field for recognizing emotional states in facial expressions. Here, we propose a novel, yet effective method, that utilizes variable decision thresholds at the prediction stage of a binary learning method for AU detection. The method performs a thresholding technique to find optimum values for each AU and make use of these thresholds as the decision threshold of the support vector machine (SVM) algorithm. Our experiments on Extended Cohn- Kanade (CK+) dataset show significant improvements on most of the AUs with an average F1 score of 6 .383 % compared with the baseline method.Item Use of Acoustic and Vibration Sensor Data to Detect Objects in Surveillance Wireless Sensor Networks(2017) Kucukbay, Selver Ezgi; Sert, Mustafa; Yazici, Adnan; 0000-0002-7056-4245; AAB-8673-2019Nowadays, people are using stealth sensors to detect intruders due to their low power consumption and wide coverage. It is very important to use lightweight sensors for detecting real time events and taking actions accordingly. In this paper, we focus on the design and implementation of wireless surveillance sensor network with acoustic and seismic vibration sensors to detect objects and/or events for area security in real time. To this end, we introduce a new environmental sensing based system for event triggering and action. In our system, we first design an appropriate hardware as a part of multimedia surveillance sensor node and use proper classification technique to classify acoustic and vibration data that are collected by sensors in real-time. According to the type of acoustic data, our proposed system triggers a camera event as an action for detecting intruder (human or vehicle). We use Mel Frequency Cepstral Coefficients (MFCC) feature extraction method for acoustic sounds and Support Vector Machines (SVM) as classification method for both acoustic and vibration data. We have also run some experiments to test the performance of our classification approach. We show that our proposed approach is efficient enough to be used in real life.