This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination...

In the present paper we have reported a wavelet based time-frequency multiresolution analysis of an ECG signal. The ECG (electrocardiogram), which records hearts electrical activity, is able to provide with useful information about the type of Cardiac disorders suffered by the patient depending upon the deviations from normal ECG signal pattern. We have plotted the coefficients of continuous wa...

For the Acoustic Scene Classification task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2017), we propose a novel method to classify 15 different acoustic scenes using deep sequential learning, based on features extracted from Short-Time Fourier Transform and scalogram of the audio scenes using Convolutional Neural Networks. It is the first time...

Music is a heavenly way of expressing feelings about the world. The language music has vast diversity. For centuries, people have indulged in debates to stratisfy between Western and Indian Classical Music. But through this paper, an understanding can be fabricated while differentiating types one essential characteristics Cultural Heritage. divided into two major parts, i.e. Hindustani Carnatic...

Hilbert Huang transform (HHT) is a relatively new method. It seems to be very promising for the different applications in signal processing because it could calculate instantaneous frequency and amplitude which is also important for the biomedical signals. HHT consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has ...

The short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) are used to analyze the time course of cellular motion in the inner ear. The velocity responses of individual outer hair cells and Hensen’s cells to sinusoidal and amplitude modulated (AM) acoustical signals applied at the ear canal display characteristics typical of nonlinear systems, including the generation of...

Time-frequency representations, like the spectrogram or the scalogram, are widely used to characterize dispersive waves. The resulting energy distributions, however, suffer from the uncertainty principle, which complicates the allocation of energy to individual propagation modes (especially when the dispersion curves of these modes are close to each other in the time-frequency domain). This res...

Marketing Decision Support Systems (MDSS) show a large variety in functionality and form. In this paper a scale is developed that measures the sophistication of a Marketing Decision Support System. This scale, based on Guttman's Scalogram Analysis, is hierarchical in nature: more sophisticated MDSS have (also) the capabilities of less sophisticated systems but not the other way around. Items, u...

BACKGROUND This study provides a replication of the Falls Efficacy Scale (FES) and a head-to-head comparison with the Activities-specific Balance Confidence (ABC) Scale designed to include a wider continuum of activity difficulty and more detailed item descriptors. METHODS Items for the newly developed 16-item ABC Scale were generated by 15 clinicians and 12 elderly outpatients. Psychometric ...

In this paper, we introduce a gear defect detection system using frequency analysis based on deep learning. The existing diagnosis systems acoustic use spectrogram, scalogram, and MFCC (Mel-Frequency Cepstral Coefficient) images as inputs to the convolutional neural network (CNN) model diagnose defects. However, visualized data input CNN models requires lot of computation time. Although computi...