José Luis Oropeza Rodríguez

Single-port thoracoscopic lobectomy in a nonintubated patient: the least invasive procedure for major lung resection?

OBJECTIVES General anaesthesia with single-lung ventilation was always considered a condition for thoracoscopic major pulmonary resections. However, nonintubated thoracoscopic lobectomy has been reported recently by using conventional video-assisted thoracoscopic surgery (VATS), epidural anaesthesia and vagus blockade. Here, we present a technique that reduces the surgical access trauma even more: single-incision VATS approach with no need for epidural or vagus blockade in a nonintubated patient. METHODS A 46-year old male smoker was admitted to our department for surgery. A computed tomography scan revealed a 1.5-cm nodule in the right middle lobe. A positron emission tomography scan demonstrated uptake (5.4 SUV) with no lymph node involvement. The patient was proposed for nonintubated uniportal VATS surgery. The patient received intramuscular midazolam and atropine 30 min before anaesthesia. No epidural catheter was placed. A laryngeal mask was used to control the airway and for oxygen inhalation. Sevoflurane gas and continued perfusion of remifentanil were administered for sedation. The patient was positioned in a left lateral decubitus position. The skin and the fifth intercostal space were infiltrated with levobupivacaine. RESULTS A VATS approach through a single 2.5-cm incision was made at the level of the fifth intercostal space on the right side. No intrathoracic vagus blockade was necessary. A wedge resection of a 1.5-cm tumour on the middle lobe was performed. The frozen section confirmed a carcinoid tumour and so a middle lobectomy and a lymph node dissection were completed. The total surgical time was 80 min. The chest tube was removed within the next 16 h and the patient was discharged home 36 h after the operation. CONCLUSIONS Single-port video-assisted thoracoscopic lobectomy in nonintubated patients seems to be feasible and safe, and probably represents the least invasive approach to lobectomy. Further studies are necessary to evaluate the results with a series of patients.

Noise pattern recognition of airplanes taking off: task for a monitoring system

This paper presents an original work for aircraft noise monitoring systems and it analyzes the airplanes noise signals and a method to identify them. The method uses processed spectral patterns and a neuronal network feed-forward, programmed by means of virtual instruments. The obtained results, very useful in portable systems, make possible to introduce redundancy to permanent monitoring systems. The noise level in a city has fluctuations between 50 dB (A) and 100 dB (A). It depends on the population density and its activity, commerce and services in the public thoroughfare, terrestrial and aerial urban traffic, of the typical activities of labor facilities and used machinery, which give varied conditions that must be faced of diverse ways within the corresponding normalization. The sounds or noises that exceed the permissible limits, whichever the activities or causes that originate them, are considered events susceptible to degrade the environment and the health.

Using adaptive filter and wavelets to increase automatic speech recognition rate in noisy environment

This paper shows results obtained in the Automatic Speech Recognition (ASR) task for a corpus of digits speech files with a determinate noise level immerse. In the experiments, we used several speech files that contained Gaussian noise. We used HTK (Hidden Markov Model Toolkit) software of Cambridge University in the experiments. The noise level added to the speech signals was varying from fifteen to forty dB increased by a step of 5 units. We used an adaptive filtering to reduce the level noise (it was based in the Least Measure Square -LMS- algorithm) and two different wavelets (Haar and Daubechies). With LMS we obtained an error rate lower than if it was not present and it was better than wavelets employed for this experiment of Automatic Speech Recognition. For decreasing the error rate we trained with 50% of contaminated and originals signals to the ASR system. The results showed in this paper are focused to try analyses the ASR performance in a noisy environment and to demonstrate that if we are controlling the noise level and if we know the application where it is going to work, then we can obtain a better response in the ASR tasks. Is very interesting to count with these results because speech signal that we can find in a real experiment (extracted from an environment work, i.e.), could be treated with these technique and we can decrease the error rate obtained. Finally, we report a recognition rate of 99%, 97.5% 96%, 90.5%, 81% and 78.5% obtained from 15, 20, 25, 30, 35 and 40 noise levels, respectively when the corpus mentioned before was employed and LMS algorithm was used. Haar wavelet level 1 reached up the most important results as an alternative to LMS algorithm, but only when the noise level was 40 dB and using original corpus.

Using adaptive filter to increase automatic speech recognition rate in a digit corpus

This paper shows results obtained in the Automatic Speech Recognition (ASR) task for a corpus of digits speech files with a determinate noise level immerse. The experiments realized treated with several speech files that contained Gaussian noise. We used HTK (Hidden Markov Model Toolkit) software of Cambridge University in the experiments. The noise level added to the speech signals was varying from fifteen to forty dB increased by a step of 5 units. We used an adaptive filtering to reduce the level noise (it was based in the Least Measure Square -LMS- algorithm). With LMS we obtained an error rate lower than if it was not present. It was obtained because of we trained with 50% of contaminated and originals signals to the ASR. The results showed in this paper to analyze the ASR performance in a noisy environment and to demonstrate that if we have controlling the noise level and if we know the application where it is going to work, then we can obtain a better response in the ASR tasks. Is very interesting to count with these results because speech signal that we can find in a real experiment (extracted from an environment work, i.e.), could be treated with these technique and decrease the error rate obtained. Finally, we report a recognition rate of 99%, 97.5% 96%, 90.5%, 81% and 78.5% obtained from 15, 20, 25, 30, 35 and 40 noise levels, respectively when the corpus that we mentioned above was employed. Finally, we made experiments with a total of 2600 sentences (between noisy and filtered sentences) of speech signal.

Implementation of the Macro and Micro Mechanical Cochlea Model in a GPU

For a long time, cochlea models have been an interesting area of study for scientists in different fields such as medicine, especially in otorhinolaryngology, physics and acoustic engineering, among others. That is because, in mammals, this organ is the most important element in the transduction of the sound pressure that is received by the outer and middle ear.

Using Values of the Human Cochlea in the Macro and Micro Mechanical Model for Automatic Speech Recognition

Recently the parametric representation using cochlea behavior has been used in different studies related with Automatic Speech Recognition (ASR). That is because this hearing organ in mammalians is the most important element used to make a transduction of the sound pressure that is received by the outer ear. This paper shows how the macro and micro mechanical model is used in ASR tasks. The values that Neely, Elliot and Ku founded in their works, related with the macro and micro mechanical model such as Neely were used to set the central frequencies of a bank filter to obtain parameters from the speech in a similar form as MFCC (Mel Frequency Cepstrum Coefficients) has been constructed.

Using a Model of the Cochlea Based in the Micro and Macro Mechanical to Find Parameters for Automatic Speech Recognition

Recently the parametric representation using cochlea behavior has been used in different studies related with Automatic Speech Recognition (ASR). That is because this important organ of the hearing in the mammalians is the principal element used to make a transduction of the sound pressure that is received by the ear. In this paper we show how the macro and micro mechanical model is used in ASR tasks. We used the values that Neely founded in his work, related with the macro and micro mechanical model, such as was named, to set the central frequencies of a bank filter to obtain parameters from the speech used in a similar form as MFCC were constructed. We propose a new approach that considers a new form to construct the bank filter in our parametric representation. Then we used this distribution of the bank filter to have a new representation of the speech in frequency domain. It is important indicate that MFCC parameters use Mel scale to create a bank filter where central frequencies of each filter is in function of the scale mentioned above. We used the response of the Neelys model behavior to create the central frequencies of the bank filter mentioned above, then we substitute the Mel scale function by another representation. We use the place theory, and we reach a 98.5% of performance, for a task that uses isolated digits pronounced by 5 different speakers. Neelys model was used because a set of parameters of the cochlea as mass, damping and stiffness, among others, when are substituted inside the model make the response obtained is closer than von Békésy proposed in his preliminary work about principle function of the cochlea.

Simulation of a model of the basilar membrane in two dimensions for Spanish vowels.

The inner ear has the cochlea as the principal element; this is a biological element in the form of a snail, within which the mechanical energy is converted into electrical energy. This process is realized by the inner ear cells on the basilar membrane. This membrane response is different for different frequencies of excitation; the result of this process is the human audition. This paper shows a simulation of a model in two dimensions of the basilar membrane and its characteristic response when excited by the tow first formants of Spanish vowels; these formants are obtained by an analysis by mixtures of Gaussians.

Gaussian wavelet functions modulated for audible response of the human ear.

One of the most important aspects related with automatic speech recognition (ASRs) systems is to find a set of characteristics that represent speech signal; nowadays, LPC, CLPC, cepstrum, MFCC, LFCC, and Melspec, among others, have been used to solve that problem. Likewise, analyzing the audible behavior of the ear has shown to have good performance in comparison with those methods that consider the pronunciation as element to obtain speech signal representation. In this paper an analysis related with Gaussian wavelets representation based on Bark and Mel audible representations is presented. For these wavelets, a Gaussian function represents the basis decomposition function; further, we need a modulator to displace the function associated with each of the scales of analysis to the central frequency into Bark and Mel audible representations. At the same time, we use the variance parameter of the Gaussian function to adapt its bandwidth to the Bark and Mel audible representations. Finally, we show a comparison between two alternatives in time and frequency representations.

Automatic speech recognition technical for Náhuatl language isolated words.

Automatic speech recognition (ASR) represents a great expectative in communications systems where interaction between computers and humans is present. In the last three decades it has been grounded. The use of computer techniques helps people who speak in a native language or with physical limitation to make transactions, leave messages, obtain information, or control some device using voice expressions. This work shows results obtained when using different techniques of ASR into the Nahuatl language. Nahuatl language is a native language spoken in Mexico and is the most important because there are many people speaking it. Nahuatl language has around 49 variants. Preserving language is important because language is the most efficient means of transmitting a culture and it is the owners of that culture who lose the most when the language dies. Mel cepstrum coefficients, vector quantization, and hidden Markov models of continuous density were used. The experiments reported 99% accuracy in ASR on Nahuatl num...