Знаходження дефектів у масиві мікрофонів за допомогою детектору голосової активності

В даній роботі було проведено аналіз наявної інформації на тему використання детектору голосової активності для знаходження дефектів в масиві мікрофонів. На основі цього було зроблено висновок, що технології з використанням голосових помічників доволі розповсюджена і продовжує поширюватися, так як це дозволяє користувачам зручно взаємодіяти з системами без використанням прямого вводу даних. Проте через кількість даних пристроїв з голосовим способом вводу з’являється проблема їх обслуговування і перевірки. Для дослідження були вивчені існуючі кодери, які використовуються в мобільному зв’язку, їх властивості, методи обробки, принципи роботи і недоліки. Для відтворення роботи детектора використовувався програмний пакет MATLAB. Була представлена робота із програмним доповненням для системи розумного будинку Amazon тм Dot, як приклад використання програмного забезпечення для роботи із одним елементом масиву мікрофонів.In this thesis, an analysis of available information on the use of a voice activity detector to find defects in the array of microphones was performed. Based on this, it was concluded that technology using voice assistants is quite common and continues to spread, as it allows users to easily interact with systems without the use of direct data input. However, due to the number of data devices with voice input, there is a problem with their maintenance and verification. This study examined existing coders used in mobile communications, their properties, processing methods, principles of operation and shortcomings. MATLAB software package was used to reproduce the detector. Work with the software add-on for the Amazon тм Dot smart home system was presented as an example of using software to work with one element of an array of microphones

Виявлення голосової активності в звуковому сигналі

Метою роботи була розробка та реалізація алгоритму виявлення голосової активності в звуковому сигналі, що дасть змогу користувачу аналізувати вхідний аудіосигнал в режимі реального часу або завантажити свій аудіофайл і провести його аналіз. Всі обчислення проводяться на основі алгоритмів аналізу енергетичних характеристик сигналу. Після проведення аналізу користувач отримує графічне відображення отриманих результатів. Користувацький додаток представляє собою вікно, за допомогою якого користувач вводить вхідну інформацію та отримує результат. Записка містить 66 сторінкок, 23 рисунки, 2 формул и та 25 посилань.The purpose of the work was to develop and implement an algorithm for detecting voice activity in a sound signal, which will allow the user to analyze the incoming audio signal in real time, or download their audio file and analyze it. All calculations are made on the basis of algorithms for analyzing the energy characteristics of the signal. After the analysis, the user receives a graphic representation of the results. The custom application is a window by which the user inputs the input information and receives the result. The note contains 66 pages, 23 figures, 2 formulas and 25 references.Целью работы была разработка и реализация алгоритма выявления речевой активности в звуковом сигнале, что дает возможность пользователю анализировать входящий аудиосигнал в режиме реального времени или загружать свой аудиофайл и проводить его анализ. Все вычисления производятся на основании алгоритмов анализа энергетических характеристик сигнала. После проведения анализа пользователь получает графическое отображение полученных результатов. Пользовательское приложение представляет собой окно при помощи которого пользователь вводит входную информацию и получает результат

Development of algorithms for smart hearing protection devices

In industrial environments, wearing hearing protection devices is required to protect the wearers from high noise levels and prevent hearing loss. In addition to their protection against excessive noise, hearing protectors block other types of signals, even if they are useful and convenient. Therefore, if people want to communicate and exchange information, they must remove their hearing protectors, which is not convenient, or even dangerous. To overcome the problems encountered with the traditional passive hearing protection devices, this thesis outlines the steps and the process followed for the development of signal processing algorithms for a hearing protector that allows protection against external noise and oral communication between wearers. This hearing protector is called the “smart hearing protection device”. The smart hearing protection device is a traditional hearing protector in which a miniature digital signal processor is embedded in order to process the incoming signals, in addition to a miniature microphone to pickup external signals and a miniature internal loudspeaker to transmit the processed signals to the protected ear. To enable oral communication without removing the smart hearing protectors, signal processing algorithms must be developed. Therefore, the objective of this thesis consists of developing a noise-robust voice activity detection algorithm and a noise reduction algorithm to improve the quality and intelligibility of the speech signal. The methodology followed for the development of the algorithms is divided into three steps: first, the speech detection and noise reduction algorithms must be developed, second, these algorithms need to be evaluated and validated in software, and third, they must be implemented in the digital signal processor to validate their feasibility for the intended application. During the development of the two algorithms, the following constraints must be taken into account: the hardware resources of the digital signal processor embedded in the hearing protector (memory, number of operations per second), and the real-time constraint since the algorithm processing time should not exceed a certain threshold not to generate a perceptible delay between the active and passive paths of the hearing protector or a delay between the lips movement and the speech perception. From a scientific perspective, the thesis determines the thresholds that the digital signal processor should not exceed to not generate a perceptible delay between the active and passive paths of the hearing protector. These thresholds were obtained from a subjective study, where it was found that this delay depends on different parameters: (a) the degree of attenuation of the hearing protector, (b) the duration of the signal, (c) the level of the background noise, and (d) the type of the background noise. This study showed that when the fit of the hearing protector is shallow, 20 % of participants begin to perceive a delay after 8 ms for a bell sound (transient), 16 ms for a clean speech signal and 22 ms for a speech signal corrupted by babble noise. On the other hand, when having a deep hearing rotection fit, it was found that the delay between the two paths is 18 ms for the bell signal, 26 ms for the speech signal without noise and no delay when speech is corrupted by babble noise, showing that a better attenuation allows more time for digital signal processing. Second, this work presents a new voice activity detection algorithm in which a low complexity speech characteristic has been extracted. This characteristic was calculated as the ratio between the signal’s energy in the frequency region that contains the first formant to characterize the speech signal, and the low or high frequencies to characterize the noise signals. The evaluation of this algorithm and its comparison to another benchmark algorithm has demonstrated its selectivity with a false positive rate averaged over three signal to noise ratios (SNR) (10, 5 and 0 dB) of 4.2 % and a true positive rate of 91.4 % compared to 29.9 % false positives and 79.0 % of true positives for the benchmark algorithm. Third, this work shows that the extraction of the temporal envelope of a signal to generate a nonlinear and adaptive gain function enables the reduction of the background noise, the improvement of the quality of the speech signal and the generation of the least musical noise compared to three other benchmark algorithms. The development of speech detection and noise reduction algorithms, their objective and subjective evaluations in different noise environments, and their implementations in digital signal processors enabled the validation of their efficiency and low complexity for the the smart hearing protection application