WOLFRAM

Wolfram Language & System Documentation Center
Wolfram Language Home Page »

AudioIntervals
✖
AudioIntervals

Updated in 14.1

✖

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:
  • AlignmentAutomaticalignment of the time stamps with partitions
    FourierParameters{-1,1}Fourier parameters
    PartitionGranularity Automaticaudio 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

open allclose all

Basic Examples  (2)Summary of the most common use cases

Compute silent intervals of audio:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-o0nvmy
Out[1]=1

Find intervals where the RMS amplitude is less than 0.01:

In[3]:=3
✖
https://wolfram.com/xid/0g7ibczwwec-86mdcs
Out[3]=3

Visualize silent intervals:

In[4]:=4
✖
https://wolfram.com/xid/0g7ibczwwec-4fnf9m
Out[4]=4

Find intervals with low RMS amplitudes:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-iivioy
Out[1]=1
In[2]:=2
✖
https://wolfram.com/xid/0g7ibczwwec-s8gpmw
Out[2]=2

Visualize the resulting intervals:

In[3]:=3
✖
https://wolfram.com/xid/0g7ibczwwec-e3al3r
Out[3]=3

Scope  (4)Survey of the scope of standard use cases

Find quiet intervals using a data-dependent threshold:

In[5]:=5
✖
https://wolfram.com/xid/0g7ibczwwec-dpa546
Out[6]=6
In[7]:=7
✖
https://wolfram.com/xid/0g7ibczwwec-4zf8eo
Out[7]=7

By default, intervals of any length are returned:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-o1yknv
Out[1]=1
In[2]:=2
✖
https://wolfram.com/xid/0g7ibczwwec-xx9la1
Out[2]=2

Compute the interval durations:

In[3]:=3
✖
https://wolfram.com/xid/0g7ibczwwec-uil19i
Out[3]=3

Find only intervals longer than a specified threshold:

In[4]:=4
✖
https://wolfram.com/xid/0g7ibczwwec-e6e95u
Out[4]=4

Test multiple properties at once:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-ff3k5r
In[2]:=2
✖
https://wolfram.com/xid/0g7ibczwwec-pcvbs0
Out[2]=2

Analyze the audio track of a video:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-l7x9gq
Out[1]=1

Options  (2)Common values & functionality for each option

PartitionGranularity  (2)

Specify a partition size of 100 ms:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-qhi76r
In[12]:=12
✖
https://wolfram.com/xid/0g7ibczwwec-eciks7
Out[12]=12

Use an offset of 10 ms:

In[11]:=11
✖
https://wolfram.com/xid/0g7ibczwwec-sceiq6
Out[11]=11

Use a smoothing window:

In[10]:=10
✖
https://wolfram.com/xid/0g7ibczwwec-jagi98
Out[10]=10

Using different partitioning specifications will give different results:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-d76ocj
In[2]:=2
✖
https://wolfram.com/xid/0g7ibczwwec-o0szji
Out[2]=2

A coarse partitioning will result in a faster computation:

In[3]:=3
✖
https://wolfram.com/xid/0g7ibczwwec-52n0ga
Out[3]=3

Applications  (4)Sample problems that can be solved with this function

Delete silent intervals of audio:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-ra5bvq
Out[1]=1

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

In[2]:=2
✖
https://wolfram.com/xid/0g7ibczwwec-tk9zti
Out[2]=2

Join the extracted intervals:

In[3]:=3
✖
https://wolfram.com/xid/0g7ibczwwec-3fd4z4
Out[3]=3
In[4]:=4
✖
https://wolfram.com/xid/0g7ibczwwec-wtyip9
Out[4]=4

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

In[5]:=5
✖
https://wolfram.com/xid/0g7ibczwwec-zi7qns
Out[5]=5
In[6]:=6
✖
https://wolfram.com/xid/0g7ibczwwec-72t08c
Out[6]=6
In[7]:=7
✖
https://wolfram.com/xid/0g7ibczwwec-og7xmf
Out[7]=7

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

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-jzgu0l
Out[1]=1
In[2]:=2
✖
https://wolfram.com/xid/0g7ibczwwec-6lkjwo
Out[2]=2

Visualize the detected intervals:

In[3]:=3
✖
https://wolfram.com/xid/0g7ibczwwec-2zv2ea
Out[3]=3

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

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-g2zv91
Out[1]=1
In[2]:=2
✖
https://wolfram.com/xid/0g7ibczwwec-buxr0h
Out[2]=2

Visualize the detected intervals:

In[3]:=3
✖
https://wolfram.com/xid/0g7ibczwwec-syarl7
Out[3]=3

Detect unvoiced segments and attenuate them:

In[1]:=1
✖
https://wolfram.com/xid/0g7ibczwwec-hv6thp
Out[1]=1

Use the "VoiceActivity" property to detect unvoiced intervals:

In[2]:=2
✖
https://wolfram.com/xid/0g7ibczwwec-wbbw0g
Out[2]=2

Visualize the detected intervals:

In[3]:=3
✖
https://wolfram.com/xid/0g7ibczwwec-jc78yd
Out[3]=3

Attenuate the detected intervals:

In[4]:=4
✖
https://wolfram.com/xid/0g7ibczwwec-f0horh
Out[4]=4

Possible Issues  (1)Common pitfalls and unexpected behavior

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

In[4]:=4
✖
https://wolfram.com/xid/0g7ibczwwec-g3vopn
Out[5]=5

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

In[7]:=7
✖
https://wolfram.com/xid/0g7ibczwwec-6gkwmc
Out[7]=7
In[9]:=9
✖
https://wolfram.com/xid/0g7ibczwwec-7g47m2
Out[9]=9
Wolfram Research (2016), AudioIntervals, Wolfram Language function, https://reference.wolfram.com/language/ref/AudioIntervals.html (updated 2024).
✖
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).

✖
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.

✖
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

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

BibTeX

@misc{reference.wolfram_2025_audiointervals, author="Wolfram Research", title="{AudioIntervals}", year="2024", howpublished="\url{https://reference.wolfram.com/language/ref/AudioIntervals.html}", note=[Accessed: 26-April-2025 ]}

✖
@misc{reference.wolfram_2025_audiointervals, author="Wolfram Research", title="{AudioIntervals}", year="2024", howpublished="\url{https://reference.wolfram.com/language/ref/AudioIntervals.html}", note=[Accessed: 26-April-2025 ]}

BibLaTeX

@online{reference.wolfram_2025_audiointervals, organization={Wolfram Research}, title={AudioIntervals}, year={2024}, url={https://reference.wolfram.com/language/ref/AudioIntervals.html}, note=[Accessed: 26-April-2025 ]}

✖
@online{reference.wolfram_2025_audiointervals, organization={Wolfram Research}, title={AudioIntervals}, year={2024}, url={https://reference.wolfram.com/language/ref/AudioIntervals.html}, note=[Accessed: 26-April-2025 ]}