# All Posts

## [04-04-2020] Introducing urlstechie and its urls checking tools

Continuous testing is a vital part of any healthy software development process. Urls must always be tested and broken links must be fixed to guarantee a high quality product. Unfortunately, this is not always trivial and can be tedious. Moreover, most tools available out there cannot handle in-code urls and lack many other needed features. A while back, I ran across this particular Problem and I quickly saw the great automation opportunity this was, and how can Python and Regex help me solve this. This was the start of the urlchecker-action … from there and with the amazing @vsoch joining, the tools expanded and this was the start of the urlstechie organization.

## [19-03-2020] How to pipe an FFmpeg output and pass it to a Python variable?

When writing code, the key optimization points are speed and efficiency. I often face this dilemma when using FFmpeg with Python. For example: when I need to convert an mp3 to a wave file and then do some processing to it in Python. The simple way to do this, is by using FFmpeg to convert the mp3 input to a wave, then read the wave in Python and do process it. Although this works, but clearly it is neither optimal nor the fastest solution. In this blog post, I will present an improved solution to this inconvenience by piping the output of FFmpeg to Python and directly pass it to a numpy variable.

## [13-03-2020] Spectral leakage and windowing

Windowing is an important part of almost any signal processing system, that helps remove/ reduce spectral leakage when processing a non-periodic signal. This blog post provides a small overview of what is spectral usage, when does it occur and how to use windowing to suppress it.

## [09-02-2020] Naive voice activity detection using short time energy

An important part of speech/speaker recognition tasks is distinction of voiced segments from silent ones. This helps -for example- align phonemes with their associated voiced segments and avoid any extra information related to silence/ noise that would degrade the system’s accuracy. This problem is known as Voice Activity Detection (VAD). This blog aims to introduce voice activity detection and present simple short time energy based VAD implementation.

## [25-01-2020] Signal framing

When it comes to non-stationary signals, spectral features in short parts/ sequences are of great use. Therefore, decomposing the signal into multiple ranges is the way to go about this type of features extraction. This technique is known as frame blocking or framing. The following blog explains why do we need framing and how to do it in python.

## [30-06-2019] Diabetes detection using machine learning (part II)

In this 2nd post on detecting diabetes with the help of machine learning and using the Pima Indian diabetic database (PIDD), we will dig into testing various classifiers and evaluating their performances. We will also examine the performance improvements by the data transformations explained in the previous post.

## [30-06-2019] Diabetes detection using machine learning (part I)

Diabetes is one of the most serious health challenges today. The publicly available Pima Indian diabetic database (PIDD) have become a popular approach for testing the efficiency of machine learning algorithms 1. Within this context, this blog post is part of 2 posts providing an in depth introduction to diabetes detection using various machine learning approaches. In this first post in particular, we focus on exploring the data at hand and preparing it for machine learning related processing.

## [09-05-2019] Voice based gender recognition using Gaussian mixture models

The aforementioned implementation, uses The Free ST American English Corpus data-set (SLR45), which is a free American English corpus by Surfingtech, containing utterances from 10 speakers (5 females and 5 males).

## [01-04-2019] Authenticated encryption

With growing dependency on information technology and more at stake, the security aspect has become more vital than ever. Within this context acts authenticated encryption(AE) as a shared-key based transform whose goal is to provide secrecy, Integrity and authenticity of the encapsulated data [MB07]. AE combines traditional Symmetric Encryption (SE) with a Message Authentication Code (MAC) in different orders [CNS14]. This article introduces the different combinations of authenticated encryption and their applications.

## [03.03.2019] Encryption

Encryption transforms a plain-text into a cipher-text using an encryption key. The process of reversing this operation uses also a key and is called “decryption” [Kes17]. These operations can be explained by the following formulas: $$C=E_{k}(P)$$ and $$P=D_{k}(C)$$, where P = plain-text, C = cipher-text, E = the encryption method, D = the decryption method, and k = the key [Kes17]. There exist several types of encryption (symmetric/asymmetric) and several ciphering techniques (block/stream) and modes etc [Kes17].

## [17-02-2019] Cryptography overview

Cryptography is mostly associated with the development and creation of mathematical algorithms, which are used to insure various security aspects See [Tam06][Oak18]. It is the cornerstone of modern communications security and is based on various mathematical concepts and theories such as: number theory, computational complexity theory and probability theory [Kes17]. The following post provides a quick overview of various cryptography concepts such as encryption, decryption and hashing.