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AudioIntervals [audio]

returns audible intervals of audio.

AudioIntervals [audio,crit]

returns intervals of audio for which the criterion crit is satisfied.

AudioIntervals [audio,crit,mindur]

returns only intervals larger than the given duration mindur.

AudioIntervals [video,…]

returns only intervals from the first audio track in video.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Options  
PartitionGranularity  
Applications  
Possible Issues  
See Also
Related Guides
History
Cite this Page

AudioIntervals [audio]

returns audible intervals of audio.

AudioIntervals [audio,crit]

returns intervals of audio for which the criterion crit is satisfied.

AudioIntervals [audio,crit,mindur]

returns only intervals larger than the given duration mindur.

AudioIntervals [video,…]

returns only intervals from the first audio track in video.

Details and Options

  • AudioIntervals can be used to detect parts of an audio signal that have specific characteristics.
  • The criteria crit can either be a string specifying a high-level objective or a pure function using local audio properties.
  • High-level string settings for crit can be one of the following:
  • "Audible" audible intervals, RMS amplitude above 0.01
    "Inaudible" inaudible intervals, RMS amplitude less than or equal to 0.01
    "Loud" louder intervals, data-dependent threshold
    "Quiet" quieter intervals, data-dependent threshold
    "VoiceActivity" intervals with detected speech
    "VoiceInactivity" intervals with no detected speech
  • The criteria crit can also be a function taking #prop arguments and uses the local property "prop" for each partition selection.
  • The following properties can be used for interval selections.
  • Basic histogram properties:
  • "MaxAbs" maximum absolute value
    "Max" maximum value
    "StandardDeviation" standard deviation of values
  • Intensity properties:
  • "Power" mean of the squared values
    "RMSAmplitude" root mean square of the values
    "Loudness" the loudness using Steven's power law
    "LoudnessEBU" the loudness according to EBU momentary standard
  • Time domain properties:
  • "CrestFactor" maximum divided by the root mean square
    "Entropy" entropy of values
    "PeakToAveragePowerRatio" maximum power divided by the average power
    "ZeroCrossingRate" rate of zero crossings
    "ZeroCrossings" number of zero crossings
  • Frequency domain properties:
  • "FundamentalFrequency" estimated fundamental frequency
    "ModifiedKullbackLeibler" modified Kullback–Leibler distance between spectra of consecutive partitions
    "SpectralCentroid" centroid of the power spectrum
    "SpectralCrest" maximum divided by the mean of the power spectrum
    "SpectralFlatness" geometric mean divided by the mean of the power spectrum
    "SpectralKurtosis" kurtosis of the magnitude spectrum
    "SpectralRollOff" frequency below which most of the energy is concentrated
    "SpectralSkewness" skewness of the magnitude spectrum
    "SpectralSlope" estimated slope of the magnitude spectrum
    "SpectralSpread" measure of the bandwidth of the power spectrum
    "SpeechFundamentalFrequency" fundamental frequency optimized for speech signals
    "VoiceActivity" detected voice activity for speech signals
  • The minimum duration mindur can be a non-negative real number in seconds, a time quantity, or a samples quantity.
  • The following options can be given:
  • Alignment Automatic alignment of the time stamps with partitions
    FourierParameters {-1,1} Fourier parameters
    PartitionGranularity Automatic audio partitioning specification
  • By default, measurements are returned at the center of each partition. Using the Alignment option, measurements can be returned at the beginning (Left ) or end (Right ) of each partition.

Examples

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Basic Examples  (2)

Compute silent intervals of audio:

Wolfram Language code: a = ExampleData[{"Sound", "AltoFluteScale"}, "Audio"]

Find intervals where the RMS amplitude is less than 0.01:

Wolfram Language code: int = AudioIntervals[a, "Inaudible"]

Visualize silent intervals:

Wolfram Language code: AudioPlot[a, Epilog -> {RGBColor[1, 0, 0, .5], Rectangle[{#[[1]], -1}, {#[[2]], 1}]& /@ int}]

Find intervals with low RMS amplitudes:

Wolfram Language code: a = ExampleData[{"Sound", "AltoFluteScale"}, "Audio"]
Wolfram Language code: int = AudioIntervals[a, #RMSAmplitude < .01&]

Visualize the resulting intervals:

Wolfram Language code: AudioPlot[a, Epilog -> {RGBColor[1, 0, 0, .5], Rectangle[{#[[1]], -1}, {#[[2]], 1}]& /@ int}]

Scope  (4)

Find quiet intervals using a data-dependent threshold:

Wolfram Language code: a = Import["ExampleData/rule30.wav"]; AudioIntervals[a, "Quiet"]
Wolfram Language code: AudioPlot[a, Epilog -> {RGBColor[1, 0, 0, .5], Rectangle[{#[[1]], -1}, {#[[2]], 1}]& /@ %}]

By default, intervals of any length are returned:

Wolfram Language code: a = ExampleData[{"Sound", "AltoFluteScale"}, "Audio"]
Wolfram Language code: int = AudioIntervals[a, #RMSAmplitude < .01&]

Compute the interval durations:

Wolfram Language code: Differences /@ int//Flatten

Find only intervals longer than a specified threshold:

Wolfram Language code: AudioIntervals[a, #RMSAmplitude < .01&, 0.2]

Test multiple properties at once:

Wolfram Language code: a = ExampleData[{"Audio", "CelloScale"}, "Audio"];
Wolfram Language code: AudioIntervals[a, #RMSAmplitude > .03 && (#SpectralCentroid < 500 || #SpectralCentroid > 800)&]

Analyze the audio track of a video:

Wolfram Language code: AudioIntervals[\!\(\*VideoBox["![Video Player: ExampleData/fish.mp4](video://content-2sfji)"]\)]

Options  (2)

PartitionGranularity  (2)

Specify a partition size of 100 ms:

Wolfram Language code: a = Import["ExampleData/rule30.wav"];
Wolfram Language code: AudioIntervals[a, #Power > .001&, PartitionGranularity -> Quantity[100, "Milliseconds"]]

Use an offset of 10 ms:

Wolfram Language code: AudioIntervals[a, #Power > .001&, PartitionGranularity -> {Quantity[100, "Milliseconds"], Quantity[10, "Milliseconds"]}]

Use a smoothing window:

Wolfram Language code: AudioIntervals[a, #Power > .001&, PartitionGranularity -> {Quantity[100, "Milliseconds"], Quantity[10, "Milliseconds"], HannWindow}]

Using different partitioning specifications will give different results:

Wolfram Language code: a = ExampleData[{"Sound", "Viola"}, "Audio"];
Wolfram Language code: AudioIntervals[a, #RMSAmplitude < .01&, PartitionGranularity -> {.005, .001}]

A coarse partitioning will result in a faster computation:

Wolfram Language code: AudioIntervals[a, #RMSAmplitude < .01&, PartitionGranularity -> {.1, .1}]

Applications  (4)

Delete silent intervals of audio:

Wolfram Language code: a = ExampleData[{"Sound", "Apollo11SmallStep"}, "Audio"]

Find the intervals where the RMS amplitude is larger than a threshold:

Wolfram Language code: silentIntervals = AudioIntervals[a, #RMSAmplitude < .04&, 0.001]

Join the extracted intervals:

Wolfram Language code: AudioDelete[a, silentIntervals]
Wolfram Language code: AudioPlot[{a, %}]

It is also possible to find silent intervals using a momentary loudness definition from the EBU standard:

Wolfram Language code: silentIntervals = AudioIntervals[a, #LoudnessEBU < -23&, 0.001]
Wolfram Language code: AudioDelete[a, silentIntervals]
Wolfram Language code: AudioPlot[{a, %}]

Use the "VoiceActivity" property to detect voiced intervals in a speech signal:

Wolfram Language code: a = ExampleData[{"Audio", "MaleVoice"}, "Audio"]
Wolfram Language code: voiced = AudioIntervals[a, #VoiceActivity == 1&, .1, PartitionGranularity -> {.06, .01}]

Visualize the detected intervals:

Wolfram Language code: AudioPlot[a, PlotLayout -> "Averaged", Epilog -> {RGBColor[1, 0, 0, 0.3], Rectangle[{#[[1]], -1}, {#[[2]], 1}]& /@ voiced}]

Combine other properties such as RMS amplitude and spectral flatness to find unvoiced audio segments:

Wolfram Language code: a = ExampleData[{"Audio", "NoisyTalk"}, "Audio"]
Wolfram Language code: unvoiced = AudioIntervals[a, #RMSAmplitude < .03 && #SpectralFlatness > .0001&, .1, PartitionGranularity -> {.06, .01}]

Visualize the detected intervals:

Wolfram Language code: AudioPlot[a, Epilog -> {RGBColor[1, 0, 0, .3], Rectangle[{#[[1]], -1}, {#[[2]], 1}]& /@ unvoiced}, ImageSize -> Medium]

Detect unvoiced segments and attenuate them:

Wolfram Language code: a = ExampleData[{"Audio", "NoisyTalk"}, "Audio"]

Use the "VoiceActivity" property to detect unvoiced intervals:

Wolfram Language code: nonVoicedIntervals = AudioIntervals[a, #VoiceActivity == 0&, .1, PartitionGranularity -> {.02, .01}]

Visualize the detected intervals:

Wolfram Language code: AudioPlot[a, Epilog -> {RGBColor[1, 0, 0, .3], Rectangle[{#[[1]], -1}, {#[[2]], 1}]& /@ nonVoicedIntervals}, ImageSize -> Medium]

Attenuate the detected intervals:

Wolfram Language code: AudioJoin[Riffle[AudioFade /@ AudioTrim[a, Except@nonVoicedIntervals], 0.3×AudioTrim[a, nonVoicedIntervals]]]

Possible Issues  (1)

The criterion function will fail if the return value is not a Boolean:

Wolfram Language code: a = ExampleData[{"Audio", "PianoScale"}, "Audio"]; AudioIntervals[a, Red&]//Head

Some properties, such as "FundamentalFrequency", can have non-numeric values, so extra care is needed:

Wolfram Language code: AudioIntervals[a, #FundamentalFrequency > 260&]//Head
Wolfram Language code: AudioIntervals[a, TrueQ[#FundamentalFrequency > 260]&]
Wolfram Research (2016), AudioIntervals, Wolfram Language function, https://reference.wolfram.com/language/ref/AudioIntervals.html (updated 2024).

Text

Wolfram Research (2016), AudioIntervals, Wolfram Language function, https://reference.wolfram.com/language/ref/AudioIntervals.html (updated 2024).

CMS

Wolfram Language. 2016. "AudioIntervals." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2024. https://reference.wolfram.com/language/ref/AudioIntervals.html.

APA

Wolfram Language. (2016). AudioIntervals. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/AudioIntervals.html

BibTeX

@misc{reference.wolfram_2026_audiointervals, author="Wolfram Research", title="{AudioIntervals}", year="2024", howpublished="\url{https://reference.wolfram.com/language/ref/AudioIntervals.html}", note=[Accessed: 14-August-2026]}

BibLaTeX

@online{reference.wolfram_2026_audiointervals, organization={Wolfram Research}, title={AudioIntervals}, year={2024}, url={https://reference.wolfram.com/language/ref/AudioIntervals.html}, note=[Accessed: 14-August-2026]}
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