"Sonograph" redirects here. For the EP by Early Day Miners, see
Sonograph (EP).
Typical spectrogram of the spoken words "nineteenth century". Frequencies are shown increasing up the vertical axis, and time on the horizontal axis. The lower frequencies are more dense because it is a male voice. The legend to the right shows that the color intensity increases with the density.
A spectrogram is a visual representation of the
spectrum of
frequencies of
sound or other signal as they vary with time. Spectrograms are sometimes called sonographs, voiceprints, or voicegrams. When the data is represented in a 3D plot they may be called waterfalls.
A common format is a graph with two geometric dimensions: one axis represents
time or
RPM,[2][failed verification] the other axis is
frequency; a third dimension indicating the
amplitude of a particular frequency at a particular time is represented by the
intensity or color of each point in the image.
There are many variations of format: sometimes the vertical and horizontal axes are switched, so time runs up and down; sometimes the amplitude is represented as the height of a 3D surface instead of color or intensity. The frequency and amplitude axes can be either
linear or
logarithmic, depending on what the graph is being used for. Audio would usually be represented with a logarithmic amplitude axis (probably in
decibels, or dB), and frequency would be linear to emphasize harmonic relationships, or logarithmic to emphasize musical, tonal relationships.
3D surface spectrogram of a part from a music piece.
Spectrogram of a male voice saying 'ta ta ta'.
Spectrogram of dolphin vocalizations; chirps, clicks and harmonizing are visible as inverted Vs, vertical lines and horizontal striations respectively.
Spectrogram of an
FM signal. In this case the signal
frequency is modulated with a
sinusoidal frequency vs. time profile.
Spectrum above and waterfall (Spectrogram) below of a 8MHz wide
PAL-I Television signal.
Spectrograms of light may be created directly using an
optical spectrometer over time.
Spectrograms may be created from a
time-domain signal in one of two ways: approximated as a filterbank that results from a series of
band-pass filters (this was the only way before the advent of modern digital signal processing), or calculated from the time signal using the
Fourier transform. These two methods actually form two different
time–frequency representations, but are equivalent under some conditions.
The bandpass filters method usually uses
analog processing to divide the input signal into frequency bands; the magnitude of each filter's output controls a transducer that records the spectrogram as an image on paper.[3]
Creating a spectrogram using the FFT is a
digital process. Digitally
sampled data, in the
time domain, is broken up into chunks, which usually overlap, and Fourier transformed to calculate the magnitude of the frequency spectrum for each chunk. Each chunk then corresponds to a vertical line in the image; a measurement of magnitude versus frequency for a specific moment in time (the midpoint of the chunk). These spectrums or time plots are then "laid side by side" to form the image or a three-dimensional surface,[4] or slightly overlapped in various ways, i.e.
windowing. This process essentially corresponds to computing the squared
magnitude of the
short-time Fourier transform (STFT) of the signal — that is, for a window width , .[5]
Applications
Early analog spectrograms were applied to a wide range of areas including the study of bird calls (such as that of the
great tit), with current research continuing using modern digital equipment[6] and applied to all animal sounds. Contemporary use of the digital spectrogram is especially useful for studying
frequency modulation (FM) in animal calls. Specifically, the distinguishing characteristics of FM chirps, broadband clicks, and social harmonizing are most easily visualized with the spectrogram.
Spectrograms are useful in assisting in overcoming speech deficits and in speech training for the portion of the population that is profoundly
deaf[7]
By reversing the process of producing a spectrogram, it is possible to create a signal whose spectrogram is an arbitrary image. This technique can be used to hide a picture in a piece of audio and has been employed by several
electronic music artists.[10] See also
steganography.
Some modern music is created using spectrograms as an intermediate medium; changing the intensity of different frequencies over time, or even creating new ones, by drawing them and then inverse transforming. See
Audio timescale-pitch modification and
Phase vocoder.
Spectrograms can be used to analyze the results of passing a test signal through a signal processor such as a filter in order to check its performance.[11]
High definition spectrograms are used in the development of RF and microwave systems[12]
Spectrograms are now used to display
scattering parameters measured with vector network analyzers[13]
From the formula above, it appears that a spectrogram contains no information about the exact, or even approximate,
phase of the signal that it represents. For this reason, it is not possible to reverse the process and generate a copy of the original signal from a spectrogram, though in situations where the exact initial phase is unimportant it may be possible to generate a useful approximation of the original signal. The Analysis & Resynthesis Sound Spectrograph[16] is an example of a computer program that attempts to do this. The
Pattern Playback was an early speech synthesizer, designed at
Haskins Laboratories in the late 1940s, that converted pictures of the acoustic patterns of speech (spectrograms) back into sound.
In fact, there is some phase information in the spectrogram, but it appears in another form, as time delay (or group delay) which is the
dual of the
Instantaneous Frequency[citation needed].
The size and shape of the analysis window can be varied. A smaller (shorter) window will produce more accurate results in timing, at the expense of precision of frequency representation. A larger (longer) window will provide a more precise frequency representation, at the expense of precision in timing representation. This is an instance of the
Heisenberg uncertainty principle, that precision in two
conjugate variables are inversely proportional to each other.
"Sonograph" redirects here. For the EP by Early Day Miners, see
Sonograph (EP).
Typical spectrogram of the spoken words "nineteenth century". Frequencies are shown increasing up the vertical axis, and time on the horizontal axis. The lower frequencies are more dense because it is a male voice. The legend to the right shows that the color intensity increases with the density.
A spectrogram is a visual representation of the
spectrum of
frequencies of
sound or other signal as they vary with time. Spectrograms are sometimes called sonographs, voiceprints, or voicegrams. When the data is represented in a 3D plot they may be called waterfalls.
A common format is a graph with two geometric dimensions: one axis represents
time or
RPM,[2][failed verification] the other axis is
frequency; a third dimension indicating the
amplitude of a particular frequency at a particular time is represented by the
intensity or color of each point in the image.
There are many variations of format: sometimes the vertical and horizontal axes are switched, so time runs up and down; sometimes the amplitude is represented as the height of a 3D surface instead of color or intensity. The frequency and amplitude axes can be either
linear or
logarithmic, depending on what the graph is being used for. Audio would usually be represented with a logarithmic amplitude axis (probably in
decibels, or dB), and frequency would be linear to emphasize harmonic relationships, or logarithmic to emphasize musical, tonal relationships.
3D surface spectrogram of a part from a music piece.
Spectrogram of a male voice saying 'ta ta ta'.
Spectrogram of dolphin vocalizations; chirps, clicks and harmonizing are visible as inverted Vs, vertical lines and horizontal striations respectively.
Spectrogram of an
FM signal. In this case the signal
frequency is modulated with a
sinusoidal frequency vs. time profile.
Spectrum above and waterfall (Spectrogram) below of a 8MHz wide
PAL-I Television signal.
Spectrograms of light may be created directly using an
optical spectrometer over time.
Spectrograms may be created from a
time-domain signal in one of two ways: approximated as a filterbank that results from a series of
band-pass filters (this was the only way before the advent of modern digital signal processing), or calculated from the time signal using the
Fourier transform. These two methods actually form two different
time–frequency representations, but are equivalent under some conditions.
The bandpass filters method usually uses
analog processing to divide the input signal into frequency bands; the magnitude of each filter's output controls a transducer that records the spectrogram as an image on paper.[3]
Creating a spectrogram using the FFT is a
digital process. Digitally
sampled data, in the
time domain, is broken up into chunks, which usually overlap, and Fourier transformed to calculate the magnitude of the frequency spectrum for each chunk. Each chunk then corresponds to a vertical line in the image; a measurement of magnitude versus frequency for a specific moment in time (the midpoint of the chunk). These spectrums or time plots are then "laid side by side" to form the image or a three-dimensional surface,[4] or slightly overlapped in various ways, i.e.
windowing. This process essentially corresponds to computing the squared
magnitude of the
short-time Fourier transform (STFT) of the signal — that is, for a window width , .[5]
Applications
Early analog spectrograms were applied to a wide range of areas including the study of bird calls (such as that of the
great tit), with current research continuing using modern digital equipment[6] and applied to all animal sounds. Contemporary use of the digital spectrogram is especially useful for studying
frequency modulation (FM) in animal calls. Specifically, the distinguishing characteristics of FM chirps, broadband clicks, and social harmonizing are most easily visualized with the spectrogram.
Spectrograms are useful in assisting in overcoming speech deficits and in speech training for the portion of the population that is profoundly
deaf[7]
By reversing the process of producing a spectrogram, it is possible to create a signal whose spectrogram is an arbitrary image. This technique can be used to hide a picture in a piece of audio and has been employed by several
electronic music artists.[10] See also
steganography.
Some modern music is created using spectrograms as an intermediate medium; changing the intensity of different frequencies over time, or even creating new ones, by drawing them and then inverse transforming. See
Audio timescale-pitch modification and
Phase vocoder.
Spectrograms can be used to analyze the results of passing a test signal through a signal processor such as a filter in order to check its performance.[11]
High definition spectrograms are used in the development of RF and microwave systems[12]
Spectrograms are now used to display
scattering parameters measured with vector network analyzers[13]
From the formula above, it appears that a spectrogram contains no information about the exact, or even approximate,
phase of the signal that it represents. For this reason, it is not possible to reverse the process and generate a copy of the original signal from a spectrogram, though in situations where the exact initial phase is unimportant it may be possible to generate a useful approximation of the original signal. The Analysis & Resynthesis Sound Spectrograph[16] is an example of a computer program that attempts to do this. The
Pattern Playback was an early speech synthesizer, designed at
Haskins Laboratories in the late 1940s, that converted pictures of the acoustic patterns of speech (spectrograms) back into sound.
In fact, there is some phase information in the spectrogram, but it appears in another form, as time delay (or group delay) which is the
dual of the
Instantaneous Frequency[citation needed].
The size and shape of the analysis window can be varied. A smaller (shorter) window will produce more accurate results in timing, at the expense of precision of frequency representation. A larger (longer) window will provide a more precise frequency representation, at the expense of precision in timing representation. This is an instance of the
Heisenberg uncertainty principle, that precision in two
conjugate variables are inversely proportional to each other.