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Principles of Digital Audio

Principles of Digital Audio. Analog Audio. 3 Characteristics of analog audio signals: Continuous signal – single repetitive waveform Infinite – sound propagates as long as oscillator is activated Measured in voltage . Digital Audio. 3 Characteristics of digital audio:

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Principles of Digital Audio

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  1. Principles of Digital Audio

  2. Analog Audio • 3 Characteristics of analog audio signals: • Continuous signal – single repetitive waveform • Infinite – sound propagates as long as oscillator is activated • Measured in voltage

  3. Digital Audio • 3 Characteristics of digital audio: • Discrete – made up of numerous samples taken from a continuous analog signal • Finite – has a defined start and end point in time • Measured in binary digits (0 and 1)

  4. Binary System • Binary code is a coding system using messages made up of strings of the digits 0 and 1 • 1 binary digit (or a single instance of 0 or 1) is referred to as 1 bit (or binary digit) • 8 bits = 1 byte

  5. Digital Conversion • Analog signals can be converted to digital signals using an analog to digital converter (ADC) • Takes numerous measurements of a signal at regular time points • In order to hear a digital signal it must be converted back into an analog signal, using an analog to digital converter (DAC) • Take the sampled digital audio and converts it into a continuous signal to be output through loudspeakers, headphones, etc.

  6. Digital Conversion • What is the benefit of converting analog signals into digital signals? • Eliminates the need for physical oscillators and large cumbersome electronic equipment • Allows for more accurate timing in processing • Allows for more refined processing methods without the need for external hardware (such as a physical delay module, flanger, phaser, pitch shifter, etc.)

  7. Sampling • The digital conversion process makes use of the process of sampling, or taking measurements of a signal at regular points in time (see analog to digital converter on previous slide) • In the sampling process, a continuous signal is divided into equal segments. Segments are then reassembled inside various digital platforms (computer programs, audio recorders, sampling units, etc.) and create a recorded representation of the original signal.

  8. Sampling • INSERT PITCURE OF SAMPLED AUDIO SIGNAL

  9. Characteristics of Digital Audio • 2 measurements characterize digital audio signals: • Sampling rate/sample frequency • Quantization (also known as bit depth) • Sampling rate – how often an audio signal is sampled • Measured in samples per second, the more samples per second, the accurate the representation of the original signal. • Bit rate/Quantization – number of bits per sample • Refers to the overall resolution and dynamic range of a signal. Higher bit depth yields wider dynamic range, lower bit depth results in limited dyanmic range (1 bit audio results in an on or off signal)

  10. Nyquist Theorem • To digitally represent a signal containing frequency components up to X Hz, the sampling rate must be at least 2X Hz • Maximum frequency perspective refers to sampling frequency in reference to the highest frequency in the signal, for example: The maximum frequency sampled at SR is SR/2 Hertz. • The 2x frequency is also called the Nyquist frequency

  11. Types of Sampling Undersampling • Undersampling contains frequency content that is beyond the sampling rate • In undersampling there are actually frequencies in the original signal that are not captured and represented in the sampled digitized version of the signal • In other words, undersampling is bad and results in digital distortion and aliasing

  12. Undersampling • INSERT IMAGE OF UNDERSAMPLING AND DIGITAL PHOTOGRAPHY ALIASING

  13. Critical Sampling • Critical sampling is when the sample rate and the highest frequency in the original signal are the same value • This may capture the original signal with no problem, but could result in distortion, aliasing and foldover • Because the original signal hits 0 points (when the sample rate and signal are at the same frequency) • Foldover – when the signal moves more quickly or as quickly as the sampling rate

  14. Critical Sampling • INSERT IMAGE OF CRITICAL SAMPLING HERE

  15. Oversampling • When the sampling rate is higher than the highest frequency in the original signal • Oversampling captures all audible frequencies in the original signal and some that are outside the audible frequency band. • Oversampling is the most desirable method of sampling • Sample rates: 44.1 kHz, 48 kHz, 96 kHz • CD audio sampling rate is 44.1 kHz

  16. History of Sampling Rate • Early digital master recordings were stored on magnetic video tape • Bits were stored as black and white pixels on the tape • The sampling rate was determined with the following figures: • 525 lines per frame with 35 blank lines = 490 lines per frame • 3 samples are stored on each line • Tape is taken at 60 fields per second, at 245 lines per frame (490/2) • So, 3 x 245 x 60 = 44100 (in other words, 44.1 kHz)

  17. Quantization • Resolution with each sample is recorded • Dependent of how many bits are available to represent the signal data • Determines the amount of original signal to amount of unwanted noise • The unwanted noise is referred to as quantization error, and is unavoidable in the digital conversion process • Quantization error is also referred to as the signal-to-noise ratio

  18. Signal-to-Noise Ratio • Ratio of original signal to amount of quantization error • Dependent on the nature of the audio content • Quantization error is less noticeable in high-level signals • It is more obvious in low-level signals. Why? Because low-level signals don’t use all available bits in the conversion process? • Less bits means the signal-to-error level is greater and quantization error becomes audible • Problems with quantization error result in digital distortion and low-level noise (we’ll talk about how to remove this from recordings later)

  19. Dither • Dither – low-level noise added to signal before it is sampled • What? Adding noise to the signal? • Adds random error to the signal. Transforms quantization error into added noise, and makes noise becomes a constant factor. • This noise can be removed to the point that it is not audible, but still removes quantization error from low-level recordings, helping them to sound cleaner with less system noise

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