Audio signal processing Flashcards
How we can decompose a signal into its frequencies
A signal can be decomposed into its frequencies using a technique called Fourier analysis. The most common method is the Fourier Transform, which converts a signal from the time domain to the frequency domain. This process results in a spectrum, which is a representation of the distribution of energy across different frequencies in the signal. The resulting spectrum can be used to identify the different frequencies present in the signal, and their relative amplitudes. Another method is Short Time Fourier Transform(STFT) which is useful when the signal is not stationary and the frequency content changes over time.
What we need to obey when digitizing a signal (And what the Nyquist-Shannon-Theorem states to answer this question)
When digitizing a signal, it is important to use a high enough sampling rate to capture all the details of the original signal. The Nyquist-Shannon theorem states that the sampling rate should be at least twice the highest frequency present in the signal to avoid distortion and loss of information. This ensures that the digitized version of the signal is a accurate representation of the original signal.
What the sampling rate of a signal is and why it is at least 40 kHz for high quality audio
The sampling rate of a signal is the number of samples of the signal that are taken per second. In audio signals, the sampling rate is typically measured in kHz (kilohertz). A higher sampling rate means that more samples are taken per second, resulting in a more accurate representation of the original signal.
For high quality audio, the sampling rate must be at least 40 kHz. This is because the human ear can hear frequencies up to 20 kHz, and in order to accurately capture and reproduce these high frequencies, a sampling rate of at least 40 kHz is required. This is the standard sampling rate used in professional audio production and it can capture all the audible frequencies by human ear.
What decomposing a signal into its frequencies has to do with audio equalizers
Decomposing a signal into its frequencies, is used to adjust the balance of different frequency bands in an audio signal using an audio equalizer. It is performed by breaking down the signal into individual frequency components and adjusting the amplitude of specific frequency bands, to achieve a desired sound. This is done through a process called frequency analysis, typically using Fourier Transform which results in a spectrum representation of the signal.
How we can use filters to modify the frequency content of a signal
Filters are used to modify the frequency content of a signal by selectively passing or blocking certain frequencies to achieve different effects such as removing unwanted noise, emphasizing or de-emphasizing certain frequencies, or isolating specific frequency ranges.
What basic filter types exist and what they do
There are various types of filters such as low-pass, high-pass, band-pass, and band-stop. Each of these filters is designed to pass or block different frequency ranges. These filters can be used to modify the frequency content of a signal to achieve different effects, such as removing unwanted noise, emphasizing or de-emphasizing certain frequencies, or isolating specific frequency ranges.
What the harmonics and the fundamental frequency are and what they tell us
The fundamental frequency is the basic sound that you hear, it is the main pitch of the sound. Harmonics are extra sounds that you can hear along with the fundamental frequency, they give the sound its unique character or quality. Together they help us understand the source of the sound and how it was made.
What a spectrogram is and what we can learn from its analysis
A spectrogram is a graph that shows how the frequencies in a sound change over time. It helps us understand the different sounds that make up the overall sound and how they change. It can be used in many fields such as speech analysis, bioacoustics, and vibration analysis.
How the STFT works and how changing the window size will impact it
STFT breaks a signal into small segments and applies a Fourier Transform to each segment to obtain the frequency content at that specific moment in time. Changing the window size will impact the time resolution and frequency resolution of the STFT. A smaller window size will give better time resolution but lower frequency resolution, while a larger window size will give better frequency resolution but lower time resolution.
