Méthode temps-fréquence de séparation aveugle de sources basée sur la fonction de cohérence segmentée

- Nous introduisons dans ce papier une nouvelle méthode de séparation aveugle de sources (SAS) concernant les mélanges linéaires instantanés. Cette approche est basée sur l'analyse de la fonction de cohérence fréquentielle réelle des signaux observés, qui est segmentée temporellement et permet de détecter les zones temps-fréquence (TF) où une seule source est active. Par ailleurs, cette méthode suppose seulement que les sources sont non corrélées. L'identification des coefficients de séparation est réalisée par le calcul de rapports de densités spectrales de puissance des signaux mélangés, dans des zones TF mono-sources. Cette approche fournit de très bonnes performances pour des mélanges de signaux de parole et/ou de bruit, avec des améliorations de rapports signal/bruit (SNRI) de 40 à plus de 90 dB et des taux de reconnaissance automatique de la parole de 100 %

An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR

A method for components instantaneous frequency (IF) estimation of multicomponent signals in low signal-to-noise ratio (SNR) is proposed. The method combines a new proposed modification of a blind source separation (BSS) algorithm for components separation, with the improved adaptive IF estimation procedure based on the modified sliding pairwise intersection of confidence intervals (ICI) rule. The obtained results are compared to the multicomponent signal ICI-based IF estimation method for various window types and SNRs, showing the estimation accuracy improvement in terms of the mean squared error (MSE) by up to 23%. Furthermore, the highest improvement is achieved for low SNRs values, when many of the existing methods fail.Scopu

Noninvasive methods for children\u27s cholesterol level determination

Today, there is a controversy about the role of cholesterol in infants and the measurement and management of blood cholesterol in children. Several scientific evidences are supporting relationship between elevated blood cholesterol in children and high cholesterol in adults and development of adult arteriosclerotic diseases such as cardiovascular and cerebrovascular disease. Therefore controlling the level of blood cholesterol in children is very important for the health of the whole population. Non-invasive methods are much more convenient for the children because of their anxieties about blood examinations. In this paper we will present a new try to find non-invasive methods for determining the level of blood cholesterol in children with the use of intelligent system

Surface EMG decomposition using a novel approach for blind source separation

We introduce a new method to perform a blind deconvolution of the surface electromyogram (EMG) signals generated by isometric muscle contractions. The method extracts the information from the raw EMG signals detected only on the skin surface, enabling longtime noninvasive monitoring of the electromuscular properties. Its preliminary results show that surface EMG signals can be used to determine the number of active motor units, the motor unit firing rate and the shape of the average action potential in each motor unit

Analisis Pemisahan Sinyal Tercampur di Bawah Air Menggunakan Metode Blind Source Separation (BSS) pada Tangki Uji Mini Semi-Tanpa Gaung (Semi-Anechoic)

Thesis ini berisi laporan eksperimen perekaman suara tercampur di bawah air berkonfigurasi overdetermined dengan jumlah sensor tiga buah dan jumlah sumber dua buah menggunakan tiga skenario keadaan untuk kemudian diurai kembali sehingga didapatkan sinyal penyusunnya menggunakan teknik Blind Source Separation (BSS) algoritma joint diagonalization time-frequency blind source separation (TFBSS) dan alternating least squares (ALS). Algoritma Time-Frequency Blind Source Separation (TFBSS) dalam memisahkan suara mendapatkan sistem pengurai (demixing matrix) dari eigenvalue dan eigenvector autokorelasi sinyal observasi, sedangkan algoritma Alternating Least Squres (ALS) mendapatkan sistem pengurai (demixing matrix) dari cross spectral density dan korelasi dari sinyal observasi. Perbedaan kedua algoritma tersebut berada pada adanya algoritma adjusting permutation pada ALS sedangkan pada TFBSS tidak. Hasil eksperimen menunjukkan bahwa unjuk kerja algoritma ALS konsisten lebih baik pada variasi suhu maupun salinitas serta kedua parameter eror yaitu MSE dan SIR dibandingkan dengan algoritma TFBSS ketika digunakan untuk memisahkan sinyal observasi yang direkam dari tangki uji mini semi-tanpa gaung. Skenario pertama yaitu variasi suhu, nilai MSE terkecil berada pada variasi sinyal observasi tipe I, penggunaan metode ALS pada suhu 21℃ yaitu sebesar 0.0966. Berdasarkan rata-rata nilai MSE metode ALS juga memiliki nilai lebih kecil yaitu sebesar 0.55 dibanding nilai rata-rata MSE TFBSS yaitu 0.6. Konsisten dengan skenario pertama, skenario kedua yaitu variasi salinitas memiliki nilai MSE terkecil pada variasi sinyal observasi tipe I, penggunaan metode ALS pada salinitas 3.1% yaitu sebesar 0.044 serta nilai rata-rata MSE metode ALS memiliki nilai lebih kecil yaitu sebesar 0.42 dibanding nilai rata-rata MSE TFBSS yaitu 0.56. Sedangkan dalam analisis nilai SIR baik pada variasi suhu maupun variasi salinitas hasil pemisahan suara menggunakan metode ALS memiliki nilai rata-rata SIR 21 dB sehingga antara sinyal estimasi satu dengan sinyal estimasi lainnya memiliki perbedaan 4 kali lebih keras ketika diterima oleh telinga, berbeda jauh dengan nilai rata-rata SIR metode TFBSS yang sebesar 3 dB. Skenario ketiga dimana perekaman percampuran suara di bawah air dilakukan pada tangki uji besar berdimensi 200×10×5.5 m tanpa variasi pada medium airnya menunjukkan adanya anomali pada hasil unjuk kerja teknik BSS kedua algoritma ALS dan TFBSS baik dari segi nilai MSE maupun SIR. Hasil skenario ketiga menunjukkan hal yang berkebalikan dari yang terjadi pada skenario pertama dan kedua yaitu nilai rata-rata MSE algoritma TFBSS yang memiliki nilai rata-rata MSE lebih kecil yaitu 0.013 dibanding rata-rata nilai MSE algoritma ALS sebesar 0.34. Hasil nilai rata-rata absolut selisih desibel dari SIR metode ALS yaitu 5.8 dB lebih besar dibandingkan nilai rata-rata absolut selisih desibel dari SIR metode TFBSS yaitu 2.5 dB. Didapatkan kesimpulan bahwa dimensi dan kondisi tempat percampuran suara memiliki pengaruh lebih signifikan dalam keberhasilan proses pemisahan suara tercampur di bawah air dibandingkan dengan variasi suhu dan salinitas pada medium air. ==================================================================================================================In this thesis, we report the sound mixed recording in underwater overdetermined configured with the number of sensors three and the number of sources two using three scenarios, then we separate again using Blind Source Separation (BSS) method with specific algorithm joint diagonalization time-frequency blind Source separation (TFBSS) and alternating least squares (ALS). When separating the mixtures, Time-Frequency Blind Source Separation (TFBSS) algorithm gets demixing matrix from the eigenvalue and eigenvector autocorrelation observation signal, while the Alternating Least Squares (ALS) algorithm gets a demixing matrix from cross spectral density and correlation the observation signal. The difference between the two algorithms is in the presence of adjusting permutation algorithm in ALS whereas in TFBSS it is not. The experimental results show that the performance of the ALS algorithm is consistently better on both temperature and salinity variations as well as the two error parametere MSE and SIR compared with the TFBSS algorithm when used to separate the observed signals recorded from mini semi-anechoic test tank. For the first scenario temperature variation, the smallest MSE value is in variation of type I observation signal, using ALS method at temperature 21℃ that is equal to 0.0966. Based on the average value, MSE ALS method also has a smaller value that is equal to 0.55 compared to the mean value of MSE TFBSS is 0.6. Consistent with the first scenario, the second scenario of salinity variation has the smallest MSE value on the variation of type I observation signal, using ALS method on salinity 3.1% which is equal to 0.044 and the average value of MSE ALS method has a smaller value that is 0.42 compared to the average value of MSE TFBSS is 0.56. While in SIR value analysis both in temperature variation and variation of salinity results using ALS method have mean value of SIR 21 dB so that between estimation signal one with other estimation signal have difference 4 times louder when received by ear, far different with average value of SIR from TFBSS method that is equal to 3 dB. For a third scenario where the recording of sound mixing is performed on a large dimension test tank 200×10×5.5 m without variation on the water medium indicates an anomaly in the results of the BSS technique's performance for both ALS and TFBSS algorithms in terms of MSE and SIR values. In this third scenario occurs the opposite of what happens in the first scenario and the second where the average value of MSE TFBSS algorithm is 0.013 smaller when compared to the average value of MSE ALS algorithm that is equal to 0.34. While for the absolute average value of the decibel difference of SIR the ALS method have value 5.8 dB greater when compared with the absolute average value of the decibel difference of SIR of TFBSS method that is equal to 2.5 dB. It is concluded that the dimensions and conditions of the mixing sound have a more significant influence in the success of the sound separation process from underwater than the variations in temperature and salinity on the water medium

Modélisation cyclostationnaire et séparation de sources des signaux électromyographiques

The aim of this thesis is to develop decomposition methods of electromyographic (EMG) signals into elementary signals, called motor unit action potential trains (MUAPT). We proposed two signal generation models and we have demonstrated the cyclostationary and fuzzy cyclostationary properties of these. We finally proposed a blind decomposition method from multi-sensor EMG signals using these properties. We present the theoretical limitations of the method, in particular the existence of a limiting threshold of the discharge frequency. We conducted a performance evaluation of the proposed method with a comparison with conventional 2nd order separation method. It has been shown that the contribution of cyclostationarity property brings better performance in noisy and noiseless cases and in the cyclostationary and fuzzy cyclostationary model cases. We highlighted a performance degradation when the discharge frequency was beyond the theoretical threshold. This evaluation was performed via Monte Carlo simulations based on real observations. Finally, we presented real EMG signals results. The method has shown good results on intramuscular EMG signals.L’objectif de cette thèse est de développer des méthodes de décomposition des signaux électromyographiques (EMG) en signaux élémentaires, les trains de potentiels d’action d’unité motrice (TPAUM). Nous avons proposé deux modèles de génération des signaux et nous avons mis en évidence la propriété de cyclostationnarité et de cyclostationnarité floue de ces deux modèles. Dans l’objectif de la décomposition, nous avons enfin proposé une méthode de décomposition aveugle à partir de signaux EMG multi-capteurs en utilisant cette propriété. Nous présentons les limitations théoriques de la méthode, notamment par un seuil limite de la fréquence de décharge. Nous avons effectué une évaluation des performances de la méthode proposée avec comparaison à une méthode classique de séparation à l’ordre 2.Il a été montré que l’exploitation de la propriété de cyclostationnarité apportait de meilleures performances de séparation dans le cas bruité et non bruité, sur le modèle cyclostationnaire et sur le modèle cyclostationnaire flou. Les performances se trouvent dégradées lorsque la fréquence de décharge dépasse le seuil théorique. Cette évaluation a été réalisée au moyen de simulations de Monte-Carlo construites sur des observations réelles. Enfin, la méthode appliquée sur des données réelles a montré de bons résultats sur des signaux EMG intramusculaires