**5. Conclusions**

96 Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

We could also have added results as if they were for a patient not coming for the first time. We choose an already existing patient, "Man 1", by choosing from the list and clicking on "Save Results…". A graph showing all the results saved to date from previous sessions is provided (pitch information is separated from that on jitter and shimmer as they are different units):

Finally, when this window is closed (top-right x), we are provided with a message

Fig. 15. Evolution of results by session

informing us on evolution since the previous session:

Fig. 16. Message providing information on evolution since last session

Due to the great relevance of Wavelet Transform for the analysis and processing of esophageal speech, and assuming that the final goal will be the implementation in a hardware DSP based device, with very strict real-time requirements, a significant computing resources optimization has been achieved, and consequently, a reduction of the code length in order to minimize computational load. Also it is important to highlight that the obtained wavelet calculi can be used in later processing.

These advantages are achieved through a preprocessing algorithm, which, although Wavelets based, includes some improvements. Firstly, an approximation to the formant subband. And secondly, an adjustable resolution applied over the bands among which the formant energy is shared.

On the other hand, the here proposed algorithm allows to optimize previous research works concerning the treatment of the poles of the system which models esophageal speech, according to LPC. Taking into account the obtained accuracy, it is logical to assume an improvement in results if this technique is used as a first stage of the whole algorithm.

**6** 

*USA* 

Richard L. Lemaster

*North Carolina State University* 

**The Use of the Wavelet Transform** 

**to Extract Additional Information on** 

**Surface Quality from Optical Profilometers** 

This chapter investigates the use of advanced signal processing techniques especially wavelet transforms to extract additional information from a two dimensional surface profile. The wavelet transform is able to aid the user in quickly assessing, visually, if the surface profile has a periodic or non-periodic component as well as if the profile signal is stationary or non-stationary. In addition, thresholds could be set at different frequencies of interest to automatically determine for the user if a periodic signal is present and if its magnitude is acceptable or not. The basis of this chapter is a doctoral dissertation by Lemaster (2004). A laser based, non-contact profilometer was used for all the surface profiles presented in this chapter though contact profilometers could also benefit from this type of analysis. The original work was conducted for wood and wood-based composites; however the signal processing techniques discussed in this chapter are applicable to all types of surfaces. In fact, an industry that would also like to determine if a surface profile is stationary or not or has periodic components is the road surface industry. They routinely use laser based optical profilometers very similar to the type used in this study except for the optics used to obtain the desired range and sensitivity. They are interested in detecting and quantifying pot holes, ruts, and washboard which are very similar to the surface characteristics of interest to the

Traditional time domain analysis that is commonly used in the analysis of surface quality does not adequately show if a periodicity exits on the surface. While frequency domain analysis can reveal if the surface has a periodicity component it cannot adequately determine if the periodicity continues across the entire surface (stationary) or if it only extends across a portion of the surface (non-stationary). This information is of importance if the user wants to extend the capability of traditional surface quality analysis and not only

Surface texture, a three-dimensional measurement, has been described as the topography, roughness, or irregularity of the interface between a substance and its surroundings,

quantify surface irregularities but classify them to both type and source.

**1. Introduction** 

**2. Background 2.1 Surface texture** 

wood industry but on a different scale.

#### **6. References**

