Spectrogram

About: Spectrogram is a research topic. Over the lifetime, 5813 publications have been published within this topic receiving 81547 citations.

Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification

Abstract: This study presents an approach to Parkinson’s disease detection using vowels with sustained phonation and a ResNet architecture dedicated originally to image classification. We calculated spectrum of the audio recordings and used them as an image input to the ResNet architecture pre-trained using the ImageNet and SVD databases. To prevent overfitting the dataset was strongly augmented in the time domain. The Parkinson’s dataset (from PC-GITA database) consists of 100 patients (50 were healthy / 50 were diagnosed with Parkinson’s disease). Each patient was recorded 3 times. The obtained accuracy on the validation set is above 90% which is comparable to the current state-of-the-art methods. The results are promising because it turned out that features learned on natural images are able to transfer the knowledge to artificial images representing the spectrogram of the voice signal. What is more, we showed that it is possible to perform a successful detection of Parkinson’s disease using only frequency-based features. A spectrogram enables visual representation of frequencies spectrum of a signal. It allows to follow the frequencies changes of a signal in time.

Automatic classification of human motions using Doppler radar

Abstract: This paper presents a new approach to classify human motions using a Doppler radar for applications in security and surveillance. Traditionally, the Doppler radar is an effective tool for detecting the position and velocity of a moving target, even in adverse weather conditions and from a long range. In this paper, we are interested in using the Doppler radar to recognize the micro-motions exhibited by people. In the proposed approach, a frequency modulated continuous wave radar is applied to scan the target, and the short-time Fourier transform is used to convert the radar signal into spectrogram. Then, the new two-directional, two-dimensional principal component analysis and linear discriminant analysis are performed to obtain the feature vectors. This approach is more computationally efficient than the traditional principal component analysis. Finally, support vector machines are applied to classify feature vectors into different human motions. Evaluated on a radar data set with three types of motions, the proposed approach has a classification rate of 91.9%.

Speech reconstruction from mel-frequency cepstral coefficients using a source-filter model

Abstract: This work presents a method of reconstructing a speech signal from a stream of MFCC vectors using a source-filter model of speech production. The MFCC vectors are used to provide an estimate of the vocal tract filter. This is achieved by inverting the MFCC vector back to a smoothed estimate of the magnitude spectrum. The Wiener- Khintchine theorem and linear predictive analysis transform this into an estimate of the vocal tract filter coefficients. The excitation signal is produced from a series of pitch pulses or white noise, depending on whether the speech is voiced or unvoiced. This pitch estimate forms an extra element of the feature vector. Listening tests reveal that the reconstructed speech is intelligible and of similar quality to a system based on LPC analysis of the original speech. Spectrograms of the MFCC-derived speech and the real speech are included which confirm the similarity.

The Hilbert–Huang Transform: A High Resolution Spectral Method for Nonlinear and Nonstationary Time Series

Abstract: Online Material: Color versions of spectrogram figures; R and hht code installation instructions with examples The Fourier transform remains one of the most popular spectral methods in time‐series analysis, so much so that the word “spectrum” is virtually equivalent to “Fourier spectrum” (Huang et al , 2001) This method assumes that a time series extends from positive to negative infinity (stationarity) and consists of a linear superposition of sinusoids (linearity) However, geophysical signals are never stationary and are not necessarily linear This results in a trade‐off between time and frequency resolution for nonstationary signals and the creation of spurious harmonics for nonlinear signals We present an open‐source implementation of the Hilbert–Huang transform (HHT), an alternative spectral method designed to avoid the linearity and stationarity constraints of Fourier analysis The HHT defines instantaneous frequency as the time derivative of phase, illuminating previously inaccessible spectral details in transient signals Nonlinear signals become frequency modulations rather than a series of fitted sinusoids, eliminating artificial harmonics in the resulting spectrogram In this paper, we describe the HHT algorithm and present our recently‐developed hht package for the R programming language This package includes routines for empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and Hilbert spectral analysis It also comes with high‐level plotting functions for easy and accurate visualization of the resulting waveforms and spectra We demonstrate this code by applying it to three signals: a synthetic nonlinear waveform, a transient signal recorded at Deception Island volcano, Antarctica, and quasi‐harmonic tremor from Reventador volcano, Ecuador The synthetic signal shows how the EMD method breaks complex time series into simpler modes It also illustrates how the Hilbert transforms of nonlinear signals produce frequency oscillations rather than harmonics The transient signal demonstrates the high‐time/frequency resolution of the HHT method The volcanic‐tremor signal has high‐frequency harmonics in the …