Advanced Time-Frequency Representation in Voice Signal Analysis

Mika, Dariusz; Jóźwik, Jerzy

doi:10.12913/22998624/87028

Cited by 15 publications

(9 citation statements)

References 35 publications

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“…where STFT mix is the m × n complex matrix of t-f containing in m-rows instantaneous signal spectra (m is the number of STFT time frames). The input data for ICA is a spectrogram (autospectrum) of the signal TFD mix = STFT mix 2 [43,44]. The rows of the TFD mix matrix are treated as individual channels in a multichannel signal.…”

Section: Model Definition and Proceduresmentioning

confidence: 99%

Single Channel Source Separation with ICA-Based Time-Frequency Decomposition

Mika

Budzik

Jóźwik

2020

Sensors

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This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the β distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal.

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Section: Model Definition and Proceduresmentioning

confidence: 99%

Single Channel Source Separation with ICA-Based Time-Frequency Decomposition

Mika

Budzik

Jóźwik

2020

Sensors

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“…In most systems, overlapping of frames is used to smooth transition from frame to frame. Each time frame is then windowed with a Hamming window to eliminate discontinuities at the edges [17]. The filter coefficients w(n) of a Hamming window of length n are computed according to:…”

Section: Mel Frequency Cepstral Coefficients (Mfcc)mentioning

confidence: 99%

“…As technology progresses, speech recognition will be embedded in more devices used in everyday activities, where environmental variables perform a major part, such as mobile phone voice recognition applications [10], cars [11], integrated access control and information systems [12], emotion identification systems [13], application monitoring [14], disabled assistance [15], and intelligent technology. In addition to voice, many acoustic applications are also essential in diverse engineering issues [16][17][18][19][20][21][22]. A noise decrease method could be deployed to enhance efficiency in real-world noisy settings [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

Alasadi¹,

Aldhayni²,

Deshmukh³

et al. 2020

Eng. Technol. Appl. Sci. Res.

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This paper studies three feature extraction methods, Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and Modified Group Delay Function (ModGDF) for the development of an Automated Speech Recognition System (ASR) in Arabic. The Support Vector Machine (SVM) algorithm processed the obtained features. These feature extraction algorithms extract speech or voice characteristics and process the group delay functionality calculated straight from the voice signal. These algorithms were deployed to extract audio forms from Arabic speakers. PNCC provided the best recognition results in Arabic speech in comparison with the other methods. Simulation results showed that PNCC and ModGDF were more accurate than MFCC in Arabic speech recognition.

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“…Time-frequency representation (TFR), broadly applied to detecting several conditions [ 14 , 15 , 16 , 17 , 18 ], has been recently used to detect pathological changes in voice signals [ 19 ]. TFR enables the evolution of the periodicity and frequency components to be observed over time, allowing the analysis of non-stationary signals, such as voice signals [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features

Tena

Clarià

Solsona

et al. 2022

Sensors

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The term “bulbar involvement” is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the quality of life and lifespan of ALS patients suffering from bulbar involvement. The main objective, and the principal contribution, of this research, was to design a new methodology, based on the phonatory-subsystem and time-frequency characteristics for detecting bulbar involvement automatically. This study focused on providing a set of 50 phonatory-subsystem and time-frequency features to detect this deficiency in males and females through the utterance of the five Spanish vowels. Multivariant Analysis of Variance was then used to select the statistically significant features, and the most common supervised classifications models were analyzed. A set of statistically significant features was obtained for males and females to capture this dysfunction. To date, the accuracy obtained (98.01% for females and 96.10% for males employing a random forest) outperformed the models in the literature. Adding time-frequency features to more classical phonatory-subsystem features increases the prediction capabilities of the machine-learning models for detecting bulbar involvement. Studying men and women separately gives greater success. The proposed method can be deployed in any kind of recording device (i.e., smartphone).

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Advanced Time-Frequency Representation in Voice Signal Analysis

Cited by 15 publications

References 35 publications

Single Channel Source Separation with ICA-Based Time-Frequency Decomposition

Single Channel Source Separation with ICA-Based Time-Frequency Decomposition

Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features

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