Overlapped Speech Detection in Multi-Party Meetings

Detection of simultaneous speech in meeting recordings is a difficult problem due both to the complexity of the meeting itself and the environment surrounding it. The system proposes the use of gammatone-like spectrogram-based linear predictor coefficients on distant microphone channel data for overlap detection functions. The framework utilized the Augmented Multiparty Interaction (AMI) conference corpus to assess model performance. The proposed system offers enhancements over base line feature set models for classification

Automatic Natural Image Segmentation by Using MarkerControlled Watershed Method and Region Merging Method

Fully automatic image segmentation is adifficult task for natural images because of manyvariations ascontrast and complex background.Conventional segmentation methods require aconsiderable amount of interactive guidance by theuser to attain satisfactory results. Moreover, the mostsubsequent tasks as object detection and imageanalyzing application highlydepend on the accurateand useful segmented result. Therefore, in this paper,an automatic image segmentation method for naturalimages is proposed. The proposed system includesthree approaches: gradient computation with themodified LoG edge filter, marker-controlled watershedsegmentation(MCWS) with automatically markerselection and region mergingapproach that is based onedge strength and homogeneous intensity. The systemcan not only efficiently reduce the significant oversegmentation problem of watershed algorithm and butalso produce the correct and meaningful segmentedimagesIt purposes better performance of segmentedimages for image annotation, objects detection, imageanalyzing task and computer vision

Sound Classification using Image Feature Extraction Technique

This paper presents texture feature extractionmethods for sound classification. Nowadays, manyresearchers interest in combination of digital signalprocessing and digital image processing fields to gethigher efficiency. In this paper, feature extractionmethods used in image processing are applied inclassification of signal processing. Signals areconverted into image format and then features areextracted using bi-directional local binary pattern.Feature vector is constructed using these featuresand then label the input signal by checking similarlyvalue from known dataset using multi support vectormachine classifier. Evaluation is tested onbenchmark dataset namely ESC10 Dataset, ESC50Dataset and UrbanSound8K Dataset.This paper presents texture feature extractionmethods for sound classification. Nowadays, manyresearchers interest in combination of digital signalprocessing and digital image processing fields to gethigher efficiency. In this paper, feature extractionmethods used in image processing are applied inclassification of signal processing. Signals areconverted into image format and then features areextracted using bi-directional local binary pattern.Feature vector is constructed using these featuresand then label the input signal by checking similarlyvalue from known dataset using multi support vectormachine classifier. Evaluation is tested onbenchmark dataset namely ESC10 Dataset, ESC50Dataset and UrbanSound8K Dataset

Analysis of Matching Pursuit Features of EEG Signal for Mental Tasks Classification

Brain Computer Interface (BCI) Systems havedeveloped for new way of communication betweencomputer and human who are suffer from severe motordisabilities and difficult to communicate with theirenvironment. BCI let them for communication by nonmuscular way. For communication between human andcomputer, BCI uses a type of signal calledElectroencephalogram (EEG) signal which arerecorded from the human‘s brain by mean of electrode.Electroencephalogram (EEG) signal is an importantinformation source for knowing brain processes for thenon-invasive BCI. In translating human’s thought, itneeds to classify acquired EEG signal accurately.Independent Component analysis (ICA) method viaEEGLab Tools for removing artifacts which are causedby eye blinks in the recorded mental task EEG signal.For features extraction, the Time and Frequencyfeatures of non stationary EEG signals are extractedby Matching Pursuit (MP) algorithm. Theclassification of mental tasks is performed byMulti_Class Support Vector Machine (SVM)