Audacity has several powerful spectral analysis tools: Spectrogram View per track, Plot Spectrum, as well as support for Plugins.
Each track in Audacity can be viewed in a Spectrogram view:
Spectrogram view of a track
To access it, click on the track menu dropdown and select Spectrogram.
The track menu also features Spectrogram settings, where you can tweak the scale, the colors, the used algorithms and the window size.
The Track Menu dropdown
Tip: To view the waveform and the spectrogram at the same time, choose Multi-view instead.
You may notice that the spectrogram is somewhat blurry usually, where even if you have a precise frequency, the spectrogram makes it look like a whole range of frequencies is being played. This is an inherent mathematical tradeoff related to the window size:
Different window sizes in comparison
Depending on what you're after, you can change the window size to fit your analysis: Smaller window sizes benefit time resolution, larger window sizes benefit frequency resolution.
Tip: If you change the algorithm from Frequencies to Reassignment, Audacity will attempt to sharpen both time and frequency resolution using the method of reassignment. This works best for signals that are separable in time and frequency with respect to the analysis window.
You can change both the window size and the algorithm in the spectrogram settings found in the Track Menu dropdown.
You can zoom vertically in Spectrogram View by right-clicking the frequency scale.
Additionally, while hovering over the frequency scale, you can
- Ctrl+Scroll to zoom in/out on the frequency scale and
- Shift+Scroll to scroll up/down while staying on the same zoom level.
To use Plot Spectrum,
- 1.select the audio you're interested in analyzing
- 2.go to Analyze -> Plot Spectrum.
The Plot Spectrum Window
- Spectrum (default) Plots the fast Fourier Transform (FFT) of the data, with the FFT window size being determined by the Size dropdown. The amplitudes are normalized such that a 0 dB sine (pure tone) will be (approximately) 0 dB on the graph.
- Autocorrelation These options measure to what extent the sound repeats itself. This is done by taking two copies of the audio, and moving one forward by one sample. The two copies are then multiplied together, and all the values added up. This is repeated for two samples difference and so on, up to the number of samples in the size option. This gives a small result if the waveform is random (for example, noise) and a large result if it is repetitive (like a musical note). By looking at the peaks in the plot, the key frequencies present can be determined even if there is a lot of noise.
- Cepstrum The cepstrum of an audio signal is related to the spectrum, but presents the rate of change in the different spectrum bands. It's particularly useful for properties of vocal tracks and is used, for example, in software to identify speakers by their voice characteristics.
Function offers choices like Rectangular, Hann, Hamming and others. We suggest you use the default Hann for most situations.
The fundamental principle at work here is that the way we observe our data changes what we see. The "true spectrum" of your project would be computed over the entire project and would provide very detailed frequency resolution but essentially no time resolution at all. In other words, this "true spectrum" would offer an average frequency distribution over the entire project. If we select a short interval of audio, the short-time spectrum has frequency resolution limited by the observation window time AND the result is affected by the spectrum of the window itself. For general audio analysis, the Rectangular window is least desirable, and the other options offer slightly different effects
Click the Export... button to export the current view as a tab-separated value text file.