In this paper, we propose a novel approach for single channel speech enhancement by exploiting the correlation among 2D transform coefficients, which has been previously neglected by traditional speech enhancement methods. Our approach makes use of a time-frequency representation (spectrogram) of the input signal and a novel 2D spectrogram filter (2DSF)is designed to provide a good estimate of ...

This paper discusses the development of a constant-Q spectrogram representation that is invertible in a least-squares sense. A good quality inverse is possible because this modified transform method, unlike the usual sliding window constant-Q spectrogram, does not discard data samples when performing the variable length discrete Fourier transforms on the signal. The development of a phase vocod...

In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis (PCA) is used to reduce the feature ...

The purpose of this study is to investigate the features of emotional speech by means of multidimensional scaling procedure(MDS) based on visual-perceived similarity of vocal parameters. We extracted three vocal parameters (pitch, intensity and spectrogram) from speeches expressed emotions. Three researchers grouped together the cards of parameters in view of visual similarity. According to the...

We investigate the use of image processing techniques based on partial differential equations applied to the image produced by timefrequency representations of one-dimensional signals, such as the spectrogram. Specifically, we use the PDE model introduced by Álvarez, Lions and Morel for noise smoothing and edge enhancement, which we show to be stable under signal and window perturbations in the...

We consider a two-dimensional demodulation framework for spectro-temporal analysis of the speech signal. We construct narrowband (NB) speech spectrograms, and demodulate them using the Riesz transform, which is a two-dimensional extension of the Hilbert transform. The demodulation results in timefrequency envelope (amplitude modulation or AM) and timefrequency carrier (frequency modulation or F...

This paper presents the spectrogram effect of biomedical signal, especially for ECG. Simulation module developed for the spectrogram implementation. Spectrogram based on ECG signal and power spectral density together with off-line evaluation has been observed. ECG contains very important clinical information about the cardiac activities of heart. The features of small variations in ECG signal w...

A tool for editing audio signals in the spectrogram is presented. It allows manipulating the spectrogram of a signal at any chosen time-frequency resolution directly and to reconstruct the edited signal in HiFi quality – a capability that is usually not possible with the Fourier or wavelet transformation. Image processing and computer vision methods are applied to the spectrogram in order to id...