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acousticToneToNoiseRatio

Identify and quantify tones in acoustic signals

Since R2023b

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    Syntax

    [tnr,tnrFreq] = acousticToneToNoiseRatio(audioIn,fs)
    [tnr,tnrFreq,isProminent] = acousticToneToNoiseRatio(audioIn,fs)
    [tnr,tnrFreq,isProminent,timestamps] = acousticToneToNoiseRatio(audioIn,fs,TimeVarying=true)
    [___] = acousticToneToNoiseRatio(___,Name=Value)
    acousticToneToNoiseRatio(___)

    Description

    example

    [tnr,tnrFreq] = acousticToneToNoiseRatio(audioIn,fs) detects tones in the audio and returns the corresponding tone-to-noise ratios (TNR) according to ECMA-418-1 [2].

    example

    [tnr,tnrFreq,isProminent] = acousticToneToNoiseRatio(audioIn,fs) also returns a logical vector specifying which tones are prominent according to ECMA-418-1.

    example

    [tnr,tnrFreq,isProminent,timestamps] = acousticToneToNoiseRatio(audioIn,fs,TimeVarying=true) detects tones and measures TNR in a time-varying signal. This syntax also returns a vector of timestamps for each time segment in the input signal.

    example

    [___] = acousticToneToNoiseRatio(___,Name=Value) specifies options using one or more name-value arguments. For example, acousticToneToNoiseRatio(x,fs,ReturnProminentOnly=true) returns only the prominent tones detected in the input signal.

    example

    acousticToneToNoiseRatio(___) with no output arguments plots the detected tones and their TNRs.

    Examples

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    Open Live Script

    Read in an audio signal containing the sound from a turbine.

    [x,fs] = audioread("Turbine-16-44p1-mono-22secs.wav");

    Listen to the first three seconds of the audio signal.

    sound(x(1:fs*3),fs)

    Use acousticToneToNoiseRatio to detect and measure tones in the stationary signal.

    • tnrFreq contains the frequencies of the detected tones in Hz.

    • tnr contains the tone-to-noise ratios of the detected tones.

    [tnr,tnrFreq] = acousticToneToNoiseRatio(x,fs);

    Call acousticToneToNoiseRatio with no output arguments to plot the tones. The plot contains data tips with information about the detected tones.

    acousticToneToNoiseRatio(x,fs)

    Open Live Script

    Read in an audio signal containing a train whistle and listen to it.

    [x,fs] = audioread("TrainWhistle-16-44p1-mono-9secs.wav");
sound(x,fs)

    Call acousticToneToNoiseRatio with TimeVarying set to true to detect the tones in the time-varying signal.

    [tnr,tnrFreq,isProminent,timestamps] = acousticToneToNoiseRatio(x,fs,TimeVarying=true);

    Call acousticToneToNoiseRatio with no output arguments to plot the time-varying tones and their tone-to-noise ratios. The plot contains data tips with information about the detected tones.

    acousticToneToNoiseRatio(x,fs,TimeVarying=true)

    Open Live Script

    Read in an audio signal containing the sound from a turbine.

    [x,fs] = audioread("Turbine-16-44p1-mono-22secs.wav");

    Use acousticToneToNoiseRatio to detect the tones in the signal. Specify the third output argument to get a logical vector indicating which tones are prominent.

    [tnr,tnrFreq,isProminent] = acousticToneToNoiseRatio(x,fs);

    Use isProminent to get the frequencies of the prominent tones. This signal has one prominent tone.

    tnrFreq(isProminent)
    ans = 1.0713e+03
    Open Live Script

    Read in an audio signal containing the sound from a turbine.

    [x,fs] = audioread("Turbine-16-44p1-mono-22secs.wav");

    Specify custom parameters for the spectral analysis performed by acousticToneToNoiseRatio.

    nfft = 2048;
window = hamming(nfft,"periodic");
overlap = round(0.75*numel(window));
freqRange = [100 16000];

    Call acousticToneToNoiseRatio with the custom parameters and see the resulting plot.

    acousticToneToNoiseRatio(x,fs,FFTLength=nfft,Window=window, ...
    OverlapLength=overlap,FrequencyRange=freqRange)

    Open Live Script

    Read in an audio signal containing the sound from a turbine.

    [x,fs] = audioread("Turbine-16-44p1-mono-22secs.wav");

    Compute the magnitude spectrum of the stationary signal and convert it to dB.

    nfft = 16384;
[pxx,f] = pwelch(x,hann(nfft,"periodic"),nfft/2,nfft,fs);
pxxDB = 10*log10(pxx);

    Use acousticToneToNoiseRatio to detect and measure tones in the spectrum.

    acousticToneToNoiseRatio(pxxDB,f);

    Open Live Script

    Read in an audio signal containing a train whistle.

    [x,fs] = audioread("TrainWhistle-16-44p1-mono-9secs.wav");

    Compute the spectrogram of the time-varying signal and convert it to dB.

    nfft = 16384;
h = hann(nfft,"periodic");
[~,fs,~,psd] = spectrogram(x,h,nfft/2,nfft,fs);
psdDB = 10*log10(psd);

    Use acousticToneToNoiseRatio to detect and measure tones in the spectrogram.

    acousticToneToNoiseRatio(psdDB,fs,TimeVarying=true)

    Input Arguments

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    Audio input, specified as a column vector or matrix. The audio input can be a time-domain or frequency-domain signal.

    • If fs is a scalar, audioIn must be a column vector, which acousticToneToNoiseRatio interprets as a single-channel time-domain signal.

    • If fs is a vector and TimeVarying is false or unspecified, audioIn must be a column vector, which acousticToneToNoiseRatio interprets as a magnitude spectrum in dB.

    • If fs is a vector and TimeVarying is true, audioIn must be a matrix, which acousticToneToNoiseRatio interprets as a magnitude spectrogram in dB. Each column in the spectrogram is a spectrum corresponding to a time segment.

    Data Types: single | double

    Sample rate or frequency vector in Hz, specified as a positive scalar or vector.

    • If fs is a scalar, it is the sample rate of the time-domain input signal audioIn. To cover the frequency range specified in the ECMA-418-1 [2] standard, the sample rate should be at least 32 kHz.

    • If fs is a vector, it contains the frequencies corresponding to each row in the frequency-domain input signal audioIn.

    Data Types: single | double

    Name-Value Arguments

    Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

    Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

    Example: acousticToneToNoiseRatio(x,fs,FrequencyRange=[500 2000])

    Time-varying input signal, specified as true or false. If TimeVarying is true, acousticToneToNoiseRatio detects and measures tones in each time segment of the time-varying signal.

    • If the input is a time-domain signal, the Window and OverlapLength determine the time segments of the time-varying signal.

    • If the input is a frequency-domain signal, each column in the magnitude spectrogram corresponds to a time segment.

    Data Types: logical

    FFT length, specified as a positive integer greater than or equal to the length of Window.

    This argument does not apply if the input is a frequency-domain signal.

    Data Types: single | double

    Analysis window, specified as a vector.

    This argument does not apply if the input is a frequency-domain signal.

    Data Types: single | double

    Overlap length, specified as a positive integer less than the length of Window. This argument specifies the number of samples overlap between adjacent windows.

    This argument does not apply if the input is a frequency-domain signal.

    Data Types: single | double

    Frequency range in Hz over which to analyze tones, specified as a two-element row vector.

    Data Types: single | double

    Flag to only return prominent tones, specified as true or false. If you specify this argument as true, acousticToneToNoiseRatio returns only tones that are prominent according to the ECMA-418-1 standard [2].

    Data Types: logical

    Flag to combine proximal tones, specified as true or false. If this argument is unspecified or true, acousticToneToNoiseRatio combines tones that are close enough to be perceived as a single tone according to the proximity criteria of the ECMA-418-1 standard.

    Data Types: single | double

    Output Arguments

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    Tone-to-noise ratios (TNR) of detected tones, returned as a vector in decreasing order. The TNR is a metric that measures perceived tonality, and it is defined in the ECMA-418-1 standard [2]. Each value in the vector is the TNR of the corresponding frequency in tnrFreq.

    If TimeVarying is true, acousticToneToNoiseRatio returns tnr as a cell array of vectors where each cell corresponds to a time segment.

    Frequencies of the detected tones in Hz, returned as a vector where each element corresponds to a TNR value in tnr.

    If TimeVarying is true, acousticToneToNoiseRatio returns tnrFreq as a cell array of vectors where each cell corresponds to a time segment.

    Prominent tones according to the ECMA-418-1 standard, returned as a logical vector with a value corresponding to each tone in tnrFreq indicating whether it is prominent.

    If TimeVarying is true, acousticToneToNoiseRatio returns isProminent as a cell array of vectors where each cell corresponds to a time segment.

    Timestamps of each time segment in the input signal in seconds, returned as a vector.

    This output argument applies only if you set TimeVarying to true.

    Algorithms

    The acousticToneToNoiseRatio function detects the tones in the input signal using the methods proposed in [1].

    References

    [1] Bray, Wade R. “Methods for Automating Prominent Tone Evaluation and for Considering Variations with Time or Other Reference Quantities.” The Journal of the Acoustical Society of America, vol. 123, no. 5, May 2008, pp. 3685–3685. DOI.org (Crossref), https://doi.org/10.1121/1.2935056.

    [2] ECMA-418-1. "Psychoacoustic metrics for ITT equipment — Part 1 (prominent discrete tones)." Ecma International. https://ecma-international.org/publications-and-standards/standards/ecma-418/.

    Extended Capabilities

    C/C++ Code Generation
    Generate C and C++ code using MATLAB® Coder™.

    Version History

    Introduced in R2023b

    See Also

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