Juan Carlos Tulli

A development and implementation of a tinnitus treatment method

Tinnitus is a physiological phenomenon where a person listens sounds which have not been generated by any external source. Today, many people suffer this condition. Although, in very few cases therapeutic methods completely eliminate tinnitus, it is possible to apply a variety of techniques to improve the quality of life of people with this condition. One of the most used methods to treat tinnitus consists of masking the tinnitus using an external sound. The main goal of this work is to present the development of a tinnitus treatment method, which optimizes the synthesized sounds in order to improve the lifes quality of the user. Subjective tests and experimental results are used to analyze the performance of the method.

Estimating RASATI scores using acoustical parameters

Acoustical analysis of speech using computers has reached an important development in the latest years. The subjective evaluation of a clinician is complemented with an objective measure of relevant parameters of voice. Praat, MDVP (Multi Dimensional Voice Program) and SAV (Software for Voice Analysis) are some examples of software for speech analysis. This paper describes an approach to estimate the subjective characteristics of RASATI scale given objective acoustical parameters. Two approaches were used: linear regression with non-negativity constraints, and neural networks. The experiments show that such approach gives correct evaluations with ±1 error in 80% of the cases.

Noise generator for tinnitus treatment based on look-up tables

Treatment of tinnitus by means of masking sounds allows to obtain a significant improve of the quality of life of the individual that suffer that condition. In view of that, it is possible to develop noise synthesizers based on random number generators in digital signal processors (DSP), which are used in almost any digital hearing aid devices. DSP architecture have limitations to implement a pseudo random number generator, due to it, the noise statistics can be not as good as expectations. In this paper, a technique to generate additive white gaussian noise (AWGN) or other types of filtered noise using coefficients stored in program memory of the DSP is proposed. Also, an implementation of the technique is carried out on a dsPIC from Microchip®. Objective experiments and experimental measurements are performed to analyze the proposed technique.

Flexible pseudorandom number generator for tinnitus treatment implemented on a dsPIC

Treatment of tinnitus with masking sounds has reach a significant developed in recent years. It is mainly because it has been possible to implement noise sinthesizers based on random number generators in digital signal processors (DSP), which form a part of almost any digital hearing aid device. One limitation of these methods is that limitations of the DSP architecture prevent pseudo white noise of being generated conform to a real white noise statistics. In this paper, a technique to generate adittive white gaussian noise (AWGN) or noise with uniform distribution using coefficients stored in memory of the DSP program is proposed. An implementation of the technique is carried out on a dsPIC from Microchip and subjective experiments and experimental measurements are performed to validate the performance of the developed technique.

Denoising Algorithms Comparison and Implementation in a Hearing Aid

In a hearing aid the Signal-to-Noise Ratio (SNR) of the output signal is an important feature. There are methods to reduce the noise contained into the speech which allow to obtain better results of the processing carried out. In this work a set of speech filtering methods are studied and compared in order to determine the best choice for this purpose. Four baseline methods are studied,which ( if necessary) use a noise power estimator developed by this application. Finally, a set of implementations are performed in both MATLAB and a low cost hearing aid device based on the dsPIC33EP256MU806 from Microchip. A set of objective experiments and experimental measures are developed to verify the performance of the system.

Low complexity noise power estimator for speech enhancement implemented on a dsPIC

In speech processing, the Signal-to-Noise Ratio (SNR) of the signal is an important feature. There are methods to reduce the noise contained into the speech which allow to obtain better results of the processing carried out. In this work a set of adaptive filtering methods are studied, with a deep analysis of the noise power estimators used to carry out the speech enhancement. Two baseline estimators are studied and a third estimator, which has lower computational complexity than the others, is presented. Finally, a set of implementations are performed in both MATLAB and a low cost hearing aid device based on the dsPIC33EP256MU806 from Microchip. A set of objective experiments and experimental measures are developed to verify the performance of the system.

Implementation of a Noise Reduction Algorithm in a Hearing Aid Device Based on a dsPIC

Nowadays there are many people affected by hearing impairments. The cost of assistive listening devices leaves out of reach for many people a technological solution. In the search of an economical alternative, low-cost DSPs arises as a good choice. However, it could be difficult to decide which has the best cost - effectiveness trade off among all devices offered in the market. The main goal of this work, then, is to determine the memory requirements of three noise reduction methods. It is done in order to select the algorithm that uses fewer resources, which will be implemented on a hearing aid device based on a low-cost DSP. This assistive listening device through digital signal processing techniques can get the basic features of a high-end assistive listening device. Objective experiments are performed to analyze the performance of the implemented system..

IMPROVING THE PERFORMANCE OF ANTI-SPAM FILTERS USING OUT-OF-VOCABULARY STATISTICS

This paper presents a feature based on out-of-vocabulary word statistics that complements the information sources used in the decision by state-of-the-art spam filters. The experiments included freely available spam filters as reference, SpamAssassin, Bogofilter, SpamBayes and SpamProbe, as well as a Naive Bayes classifier. The results show that the decision based on the proposed feature improves the performance of all spam filters under study.