Audacity spectrogram series#
Audacity spectrogram windows#

Amplitude is also included, using color or grayscale. Spectrograms are especially useful for analyzing quasi-periodic vibrations (like those in music and human speech).Ī spectrogram is usually drawn in two dimensions, with time along the horizontal axis and frequency on the vertical axis. SpectrogramsĪ spectrogram is a hybrid between an FFT and a time domain graph- it shows how the spectral content of the vibration changes over time. That’s why most FFTs are made from a sample of sound that is constant throughout. FFTs only show which frequencies are present in a sound sample- FFTs do not show when those sounds occur.

Audacity spectrogram series#

For instance, this FFT could be from a series of pure tones played one at a time in ascending order, or from a series of pure tones played in descending order. If so, all frequencies are happening simultaneously. This FFT probably represents a single complex tone played for the entire sample. Time window is also limited by the sampling rate of the recording. The time window cannot be longer than the sample.

Audacity spectrogram windows#

Longer time windows generally give more detailed FFT graphs. Most computer programs have a default setting for time window that the user can change. Time windowis a parameter used in the FFT calculation. (There’s obviously a trade-off here: the longer the sample you choose, the more likely it is that the vibration will change during your selection). You will probably never have to do the math that turns time domain graphs into frequency domain graphs, but you may have to select settings on a computer program that generates FFTs, so it’s important to recognize some of the limitations of the math.įirst, choose a sample of the time-domain graph where the vibration is not changing much. Later in the book, you will see spectrograms- a type of graph that is mixture of time- and frequency-domain graphs. Frequency domain graphs show spectral content, but contain no information about how vibrations change over time. A complete description of a changing vibration requires both time-domain and frequency-domain graphs. Time domain graphs are useful for showing how vibrations change with time, but not very useful for showing spectral content. Which of the frequencies on the graph occurred first? Explain. One of the figures above (“Frequency domain plot of a complex vibration with a fundamental frequency of 1 kHz”) shows peaks at many different frequencies. Here’s the FFT of a complex sound with a prominent fundamental at 1 kHz, an overtone at 2 kHz, an even weaker overtone at 3 kHz, and so on. Some sounds are made up of a weak fundamental and prominent overtones. While the fundamental is often the tallest peak, it does not have to be. Frequencies with shorter peaks have smaller amplitudes. Frequency components with large amplitudes have tall peaks. Other peaks indicate overtones. The height of each peak indicates how much of each frequency is present in the vibration. The left-most peak represents the fundamental. Low frequency peaks are shown at the left end of the graph. The figure below shows the FFT of a pure tone with frequency 5,000 Hz: Frequency plot for an simple vibration with a frequency of 5000 Hz.įFTs of complex vibrations have multiple peaks- one for each frequency present in the vibration. The height of the spike gives the amplitude. As a result, the FFT of a pure tone is a single vertical “spike” or “peak.” The location of the spike along the horizontal (frequency) axis indicates the frequency. Frequency domain graphs of some common soundsĪ pure tone consists of just a single frequency- all other frequencies are absent. Making a meaningful FFT requires choosing your sample carefully- for simplicity, the sound you analyze with an FFT should remain reasonably constant throughout the entire sample.

(If you want to find out which sounds occurred when, look at a time-domain graph).

All of the sounds shown on an FFT occurred at some point during the sample, but it’s impossible to tell when which one occurred. The math that transforms a time domain graph into a frequency domain graph erases all information about when the sounds occurred within the sample. The valleys show which frequencies are absent. The horizontal location of peaks indications which frequencies are strongly present in the sound. FFTs often look like a series of mountain peaks. Frequency is plotted along the x-axis and amplitude is plotted along the y-axis.

Spectrum plots are particularly useful for representing sounds, because frequency plays such a large role in hearing,įrequency domain graphs show much of each frequency is present in a sample. Vibrations 5 FFTs and spectrograms Frequency domain graphsįrequency-domain graphs– also called spectrum plots and Fast Fourier transform graphs ( FFT graphs for short)- show which frequencies are present in a vibration during a certain period of time.