Deux niveaux et deux outils d'analyse pour une meilleure segmentation de données audio

- Dans cet article, nous abordons le problème de la segmentation de données audio. Nous proposons un processus de segmentation à deux niveaux qui permet de diviser les pistes audio en courtes séquences qui sont étiquetées dans différentes classes. La segmentation est effectuée en calculant différentes caractéristiques pour chaque séquence audio. Ces caractéristiques sont calculées soit sur un segment audio complet, soit sur une trame (ensemble d'échantillons) qui est un sous-ensemble d'un segment audio. L'approche proposée pour la microsegmentation des données audio consiste en une combinaison d'un classifieur K-Means au niveau des segments et d'un système de chaînes de Markov cachées multidimensionnelles utilisant une décomposition du signal en trames. Une première classification est obtenue en utilisant le classifieur K-Means et les caractéristiques relatives aux segments. Le résultat final est alors fourni par l'utilisation des chaînes de Markov cachées multidimensionnelles et les caractéristiques relatives aux trames, en se basant sur les résultats intermédiaires fournis par la première étape. Les chaînes de Markov cachées multidimensionnelles sont une extension des chaînes de Markov cachées classiques qui permet la prise en compte de données multicomposantes. Elles sont particulièrement adaptées dans notre cas où chaque segment audio peut être représenté par plusieurs caractéristiques de différentes natures

Ultrasonic signal detection and recognition using dynamic wavelet fingerprints

A novel ultrasonic signal detection and characterization technique is presented in this dissertation. The basic tool is a simplified time-frequency (scale) projection which is called a dynamic wavelet fingerprint. Take advantage of the matched filter and adaptive time-frequency analysis properties of the wavelet transform, the dynamic wavelet fingerprint is a coupled approach of detection and recognition. Different from traditional value-based approaches, the dynamic wavelet fingerprint based technique is pattern or knowledge based. It is intuitive and self-explanatory, which enables the direct observation of the variation of non-stationary ultrasonic signals, even in complex environments. Due to this transparent property, efficient detection and characterization algorithms can be customized to address specific problems. Furthermore, artificial intelligence can be integrated and expert systems can be developed based on it.;Several practical ultrasonic applications were used to evaluate the feasibility and performance of this technique. The first application was ultrasonic materials sorting. Dynamic wavelet fingerprints of echoes from the surface of different plates were generated and then used to successfully identify corresponding plates.;The second application was ultrasonic periodontal probing. The dynamic wavelet fingerprint technique was used to expose the hidden trend of the complex waveforms. Taking the manual probing data as gold standard , a 40% agreement ratio was achieved with a tolerance limit of 1mm. However, statistically, lack of agreement was found in terms of the limits of agreement of Bland and Altman.;The third application was multi-mode Lamb wave tomography. The dynamic wavelet fingerprint technique was used to extract arrival times of transmitted Lamb wave modes. The overall quality of the estimated arrival times was acceptable in terms of their regular distributions and discernable variation patterns that correspond to specific defects. The tomographic images generated from estimated arrival times were also fine enough to indicate different defects in aluminum plates.;The last application was ultrasonic thin multi-layers inspection. High precision and robustness of a dynamic wavelet fingerprint based algorithm was demonstrated by processing simulated ultrasonic signals. When applied to practical data obtained from a plastic encapsulated IC package, multiple interfaces in the package were successfully detected

Sonar sensor interpretation for ectogeneous robots

We have developed four generations of sonar scanning systems to automatically interpret surrounding environment. The first two are stationary 3D air-coupled ultrasound scanning systems and the last two are packaged as sensor heads for mobile robots. Template matching analysis is applied to distinguish simple indoor objects. It is conducted by comparing the tested echo with the reference echoes. Important features are then extracted and drawn in the phase plane. The computer then analyzes them and gives the best choices of the tested echoes automatically. For cylindrical objects outside, an algorithm has been presented to distinguish trees from smooth circular poles based on analysis of backscattered sonar echoes. The echo data is acquired by a mobile robot which has a 3D air-coupled ultrasound scanning system packaged as the sensor head. Four major steps are conducted. The final Average Asymmetry-Average Squared Euclidean Distance phase plane is segmented to tell a tree from a pole by the location of the data points for the objects interested. For extended objects outside, we successfully distinguished seven objects in the campus by taking a sequence scans along each object, obtaining the corresponding backscatter vs. scan angle plots, forming deformable template matching, extracting interesting feature vectors and then categorizing them in a hyper-plane. We have also successfully taught the robot to distinguish three pairs of objects outside. Multiple scans are conducted at different distances. A two-step feature extraction is conducted based on the amplitude vs. scan angle plots. The final Slope1 vs. Slope2 phase plane not only separates the rectangular objects from the corresponding cylindrical