Roneel V. Sharan

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In this paper, we use the novel method of using features extracted from the time-frequency image representation of a sound signal in an audio surveillance application. In particular, we investigate two image representations: linear grayscale and log grayscale. We first divide a sound signal into smaller frames and apply a windowing function. The absolute(More)
A sound signal produces a unique texture which can be visualized using a spectrogram image and analyzed for automatic sound recognition. In this paper, we explore the use of a well-known image texture analysis technique called the gray-level co-occurrence matrix (GLCM) for sound recognition in an audio surveillance application. The GLCM captures the(More)
In this paper, we utilize time-frequency image representations of sound signals for feature extraction in an audio surveillance application. Starting with the conventional spectrogram images, we consider a new feature which is based on image texture analysis. It utilizes the gray-level co-occurrence matrix, which captures the distribution of co-occurring(More)
This paper describes the simulation of arm movements of a stepper motor controlled pickand-place robot using the mathematical model of a stepper motor. The model includes: a) a model of the stepper model driver board, b) a model of the hybrid stepper motor and load combination, and c) the interconnection of the two models which is used to simulate the(More)
In this paper, we compare the performance of classification techniques for multiclass support vector machines in an unstructured environment. In particular, we consider the following methods: one-against-all, one-against-one, decision directed acyclic graph, and adaptive directed acyclic graph. The performance is compared in terms of classification(More)
In this paper, we use the cochleagram image of sound signals for time-frequency analysis and feature extraction, instead of the conventional spectrogram image, in an audio surveillance application. The signal is firstly passed through a gammatone filter which models the auditory filters in the human cochlea. The filtered signal is then divided into small(More)
In this work, we use the subband intensity histogram values extracted from the spectrogram image of sound signals to form the feature vector for sound classification in an audio surveillance application. We propose two features based on this approach. Firstly, we extract the histogram features from the short time Fourier transform spectrogram image of sound(More)