A beginner's guide to the world of sound visualization

These resources will take a closer look at sound visualization and what you need to know about it.

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A beginner's guide to the world of sound visualization

Resources

Key quotes:

  • "1.1 Waveforms 1.2 The sine wave 1.3 Harmonics 1.4 Complex waves 2.1 Voltage Controlled Oscillators 2.2 Voltage Controlled Amplifier 2.3 Envelopes 3.1 Additive synthesis 3.2 Subtractive synthesis 3.3 Frequency Modulation (FM) 3.4 Other types of synthesis Unless you live under a rock, you’ve probably seen one of these things in your life: It’s an audio file."
  • "But these other sine waves have to have a specific amplitude and frequency to make something that sounds good."
  • "Sine wave: The simplest waveform consisting of a fundamental frequency and no harmonics."
  • "You could cut away certain frequencies to alter the harmonic spectrum of the sound, like in subtractive synthesis."
  • "Modulating amplitude: This will make the synth rise and fall in volume, or, if the LFO frequency is fast enough, create a tremolo-like sound."
  • "The physics of sound: The wiggly line of a waveform represents the vibrating motion of a sound source, which causes air molecules to collide and transmit a sound wave to your ear."
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Key quotes:

  • "This publication is the first publication for data representations for comparison and it includes — Bar Chart, Stacked, Grouped & Overlapping Bar Charts, Multiple Series 3D Bar Charts, Clustered Force Layout & Bubble Chart."
  • "The bar chart can be used to show the comparison of one variable for multiple categories or comparison for value changes over a period of time."
  • "Taking the same example as that of the stacked bar graph, the visitors on a website can be represented with grouped bar charts too."
  • "Similar to grouped bar charts, overlapping bar charts are used to represent a comparison between two categories on the basis of multiple variables."
  • "Given below is an overlapping bar chart that shows the number of visitors on an e-Commerce website along with the number of purchases."
  • "Let us plot the same example we’ve been looking at using multiple series 3D bar charts."
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Key quotes:

  • "This blog would briefly focus on the concepts that you must know about before you appear for a Data Science entry-level interview."
  • "Through the length of the blog, I will cover a list of topics that help for an entry-level data Science interview, the amount of domain knowledge you need to have about it and BONUS: questions around a topic from my interviews!!! Now Math might not be the strongest point for everyone but being a data scientist needs you to perform good on statistics, linear algebra, probability and differential calculus."
  • "Working knowledge of both SQL and noSQL systems help in an interview."
  • "It is a starting point for a beginner Data Scientist to fathom that machine learning is part of data science."
  • "I would recommend knowing what exactly the following data terms mean so when a question peripheral to that comes up, you are ready to fire! Just as it sounds, data cleaning is where you EDA is an approach to reduce data to a smaller set of summary variables and use those results to: Along with knowledge on all these, you also would have to know about the IDEs and tools you work on."
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Transcript: Let's play a game. Imagine that you are in Las Vegas, in a casino, and you decide to play a gameon one of the casino's computers, just like you might playsolitaire or chess. The computer can make movesin the game, just like a human player. This is a coin game. It starts with a coin showing heads, and the computer will play first. It can choose to flip the coin or not, but you don't get to see the outcome. Next, it's your turn. You can also chooseto flip the coin or not, and your move will not be revealedto your opponent, the computer. Finally, the computer plays again,and can flip the coin or not, and after these three rounds, the coin is revealed, and if it is heads, the computer wins, if it's tails, you win. So it's a pretty simple game, and if everybody plays honestly,and the coin is fair, then you have a 50 percent chanceof winning this game. And to confirm that, I asked my students to playthis game on our computers, and after many, many tries, their winning rate ended upbeing 50 percent, or close to 50 percent, as expected. Sounds like a boring game,... See more →

Key quotes:

  • "The below speech representation is a plot of the speech signal from ‘arctic_a0005.wav’ whose equivalent text is “will we ever forget it” : It can be seen from the above figure that speech can be represented as a variation of amplitude with time."
  • "Further we will look at the frame level frequency domain representation of speech, also popularly known as Short-time Fourier transform (STFT) in the speech research community."
  • "It can be seen from the above plot that frequency domain representation of each frame helps us to analyse speech signal better; as we can easily see the harmonics as parallel red segments in voiced regions and how the amplitude varies for each frequency and frame index."
  • "Given a speech signal s[n], silence regions can be detected by comparing the relative energy of segments within short durations."
  • "A short frame size is able to detect abrupt change in energy but may give many alternating frames of silence segments due to inherent silence parts in some phonemes like bursts and in between words."
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Key quotes:

  • "Understanding the typical work flow on how the data science pipeline works is a crucial step towards business understanding and problem solving."
  • "So before we even begin the OSEMN pipeline, the most crucial and important step that we must take into consideration is understanding what problem we’re trying to solve."
  • "No matter how well your model predicts, no matter how much data you acquire, and no matter how OSEMN your pipeline is… your solution or actionable insight will only be as good as the problem you set for yourself."
  • "Objective: Skills Required: “The man who is prepared has his battle half fought” — Miguel de Cervantes Now during the exploration phase, we try to understand what patterns and values our data has."
  • "After cleaning your data and finding what features are most important, using your model as a predictive tool will only enhance your business decision making."
  • "If you’re a parent then good news for you.Instead of reading the typical Dr."
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