pitch
Estimate fundamental frequency of audio signal
Description
specifies options using one or more name-value arguments.f0
= pitch(audioIn
,fs
,Name=Value
)
pitch(___)
with no output arguments plots the
estimated pitch against time.
Examples
Estimate Pitch
Read in an audio signal. Call pitch
to estimate the fundamental frequency over time.
[audioIn,fs] = audioread("Hey-16-mono-6secs.ogg");
f0 = pitch(audioIn,fs);
Listen to the audio signal and plot the signal and pitch. The pitch
function estimates the fundamental frequency over time, but the estimate is only valid for regions that are harmonic.
sound(audioIn,fs) tiledlayout(2,1) nexttile t = (0:length(audioIn)-1)/fs; plot(t,audioIn) xlabel("Time (s)") ylabel("Amplitude") grid minor axis tight nexttile pitch(audioIn,fs)
Estimate Pitch For Singing Voice
Read in an audio signal and extract the pitch.
[x,fs] = audioread("SingingAMajor-16-mono-18secs.ogg"); t = (0:size(x,1)-1)/fs; winLength = round(0.05*fs); overlapLength = round(0.045*fs); [f0,idx] = pitch(x,fs,Method="SRH",WindowLength=winLength,OverlapLength=overlapLength); tf0 = idx/fs;
Listen to the audio and plot the audio and pitch estimations.
sound(x,fs) figure tiledlayout(2,1) nexttile plot(t,x) ylabel("Amplitude") title("Audio Signal") axis tight nexttile pitch(x,fs,Method="SRH",WindowLength=winLength,OverlapLength=overlapLength) title("Pitch Estimations")
The pitch
function estimates the pitch for overlapped analysis windows. The pitch estimates are only valid if the analysis window has a harmonic component. Call the harmonicRatio
function using the same window and overlap length used for pitch detection. Plot the audio, pitch, and harmonic ratio.
hr = harmonicRatio(x,fs,Window=hamming(winLength,"periodic"),OverlapLength=overlapLength); figure tiledlayout(3,1) nexttile plot(t,x) ylabel("Amplitude") title("Audio Signal") axis tight nexttile pitch(x,fs,Method="SRH",WindowLength=winLength,OverlapLength=overlapLength) title("Pitch Estimations") xlabel("") nexttile harmonicRatio(x,fs,Window=hamming(winLength,"periodic"),OverlapLength=overlapLength) title("Harmonic Ratio")
Use the harmonic ratio as the threshold for valid pitch decisions. If the harmonic ratio is less than the threshold, set the pitch decision to NaN
. Plot the results.
threshold = 0.9; f0(hr < threshold) = nan; figure plot(tf0,f0) xlabel("Time (s)") ylabel("Pitch (Hz)") title("Pitch Estimations") grid on
Compare Pitch of Two Voices
Read in an audio signal of a female voice saying "volume up" five times. Listen to the audio.
[femaleVoice,fs] = audioread("FemaleVolumeUp-16-mono-11secs.ogg");
sound(femaleVoice,fs)
Read in an audio signal of a male voice saying "volume up" five times. Listen to the audio.
maleVoice = audioread("MaleVolumeUp-16-mono-6secs.ogg");
sound(maleVoice,fs)
Extract the pitch from both the female and male recordings. Plot histograms of the pitch estimations for the male and female audio recordings. The histograms have a similar shape. This is because the pitch decisions contain results for unvoiced speech and regions of silence.
f0Female = pitch(femaleVoice,fs); f0Male = pitch(maleVoice,fs); figure numBins = 20; histogram(f0Female,numBins,Normalization="probability"); hold on histogram(f0Male,numBins,Normalization="probability"); legend("Female Voice","Male Voice") xlabel("Pitch (Hz)") ylabel("Probability") hold off
Use the detectSpeech
function to isolate regions of speech in the audio signal and then extract pitch from only those speech regions.
speechIndices = detectSpeech(femaleVoice,fs); f0Female = []; for ii = 1:size(speechIndices,1) speechSegment = femaleVoice(speechIndices(ii,1):speechIndices(ii,2)); f0Female = [f0Female;pitch(speechSegment,fs)]; end speechIndices = detectSpeech(maleVoice,fs); f0Male = []; for ii = 1:size(speechIndices,1) speechSegment = maleVoice(speechIndices(ii,1):speechIndices(ii,2)); f0Male = [f0Male;pitch(speechSegment,fs)]; end
Plot histograms of the pitch estimations for the male and female audio recordings. The pitch distributions now appear as expected.
figure histogram(f0Female,numBins,Normalization="probability"); hold on histogram(f0Male,numBins,Normalization="probability"); legend("Female Voice","Male Voice") xlabel("Pitch (Hz)") ylabel("Probability")
Estimate Pitch of Musical Signal Using Nondefault Parameters
Load an audio file of the Für Elise introduction and the sample rate of the audio.
load FurElise.mat song fs sound(song,fs)
Call the pitch
function using the pitch estimate filter (PEF
), a search range of 50 to 800 Hz, a window duration of 80 ms, an overlap duration of 70 ms, and a median filter length of 10.
method = "PEF"; range = [50, 800]; % hertz winDur = 0.08; % seconds overlapDur = 0.07; % seconds medFiltLength = 10; % frames winLength = round(winDur*fs); overlapLength = round(overlapDur*fs); [f0,loc] = pitch(song,fs, ... Method=method, ... Range=range, ... WindowLength=winLength, ... OverlapLength=overlapLength, ... MedianFilterLength=medFiltLength);
Plot the estimated pitch against time.
pitch(song,fs, ... Method=method, ... Range=range, ... WindowLength=winLength, ... OverlapLength=overlapLength, ... MedianFilterLength=medFiltLength);
Determine Pitch Contour of Streaming Audio
Create a dsp.AudioFileReader
object to read in audio frame-by-frame.
fileReader = dsp.AudioFileReader("SingingAMajor-16-mono-18secs.ogg");
Create a voiceActivityDetector
object to detect the presence of voice in streaming audio.
VAD = voiceActivityDetector;
While there are unread samples, read from the file and determine the probability that the frame contains voice activity. If the frame contains voice activity, call pitch
to estimate the fundamental frequency of the audio frame. If the frame does not contain voice activity, declare the fundamental frequency as NaN
.
f0 = []; while ~isDone(fileReader) x = fileReader(); if VAD(x) > 0.99 decision = pitch(x,fileReader.SampleRate, ... WindowLength=size(x,1), ... OverlapLength=0, ... Range=[200,340]); else decision = NaN; end f0 = [f0;decision]; end
Plot the detected pitch contour over time.
t = linspace(0,(length(f0)*fileReader.SamplesPerFrame)/fileReader.SampleRate,length(f0)); plot(t,f0) ylabel("Fundamental Frequency (Hz)") xlabel("Time (s)") grid on
Compare Pitch Detection Algorithms
The different methods of estimating pitch provide trade-offs in terms of noise robustness, accuracy, optimal lag, and computation expense. In this example, you compare the performance of different pitch detection algorithms in terms of gross pitch error (GPE) and computation time under different noise conditions.
Prepare Test Signals
Load an audio file and determine the number of samples it has. Also load the true pitch corresponding to the audio file. The true pitch was determined as an average of several third-party algorithms on the clean speech file.
[audioIn,fs] = audioread('Counting-16-44p1-mono-15secs.wav'); numSamples = size(audioIn,1); load TruePitch.mat truePitch
Create test signals by adding noise to the audio signal at given SNRs. The mixSNR
function is a convenience function local to this example, which takes a signal, noise, and requested SNR and returns a noisy signal at the request SNR.
testSignals = zeros(numSamples,4); turbine = audioread('Turbine-16-44p1-mono-22secs.wav'); testSignals(:,1) = mixSNR(audioIn,turbine,20); testSignals(:,2) = mixSNR(audioIn,turbine,0); whiteNoiseMaker = dsp.ColoredNoise('Color','white','SamplesPerFrame',size(audioIn,1)); testSignals(:,3) = mixSNR(audioIn,whiteNoiseMaker(),20); testSignals(:,4) = mixSNR(audioIn,whiteNoiseMaker(),0);
Save the noise conditions and algorithm names as cell arrays for labeling and indexing.
noiseConditions = {'Turbine (20 dB)','Turbine (0 dB)','WhiteNoise (20 dB)','WhiteNoise (0 dB)'}; algorithms = {'NCF','PEF','CEP','LHS','SRH'};
Run Pitch Detection Algorithms
Preallocate arrays to hold pitch decisions for each algorithm and noise condition pair, and the timing information. In a loop, call the pitch
function on each combination of algorithm and noise condition. Each algorithm has an optimal window length associated with it. In this example, for simplicity, you use the default window length for all algorithms. Use a 3-element median filter to smooth the pitch decisions.
f0 = zeros(numel(truePitch),numel(algorithms),numel(noiseConditions)); algorithmTimer = zeros(numel(noiseConditions),numel(algorithms)); for k = 1:numel(noiseConditions) x = testSignals(:,k); for i = 1:numel(algorithms) tic f0temp = pitch(x,fs, ... 'Range',[50 300], ... 'Method',algorithms{i}, ... 'MedianFilterLength',3); algorithmTimer(k,i) = toc; f0(1:max(numel(f0temp),numel(truePitch)),i,k) = f0temp; end end
Compare Gross Pitch Error
Gross pitch error (GPE) is a popular metric when comparing pitch detection algorithms. GPE is defined as the proportion of pitch decisions for which the relative error is higher than a given threshold, traditionally 20% in speech studies. Calculate the GPE and print it to the Command Window.
idxToCompare = ~isnan(truePitch); truePitch = truePitch(idxToCompare); f0 = f0(idxToCompare,:,:); p = 0.20; GPE = mean( abs(f0(1:numel(truePitch),:,:) - truePitch) > truePitch.*p).*100; for ik = 1:numel(noiseConditions) fprintf('\nGPE (p = %0.2f), Noise = %s.\n',p,noiseConditions{ik}); for i = 1:size(GPE,2) fprintf('- %s : %0.1f %%\n',algorithms{i},GPE(1,i,ik)) end end
GPE (p = 0.20), Noise = Turbine (20 dB).
- NCF : 0.9 % - PEF : 0.4 % - CEP : 8.2 % - LHS : 8.2 % - SRH : 6.0 %
GPE (p = 0.20), Noise = Turbine (0 dB).
- NCF : 5.6 % - PEF : 24.5 % - CEP : 11.6 % - LHS : 9.4 % - SRH : 46.8 %
GPE (p = 0.20), Noise = WhiteNoise (20 dB).
- NCF : 0.9 % - PEF : 0.0 % - CEP : 12.9 % - LHS : 6.9 % - SRH : 2.6 %
GPE (p = 0.20), Noise = WhiteNoise (0 dB).
- NCF : 0.4 % - PEF : 0.0 % - CEP : 23.6 % - LHS : 7.3 % - SRH : 1.7 %
Calculate the average time it takes to process one second of data for each of the algorithms and print the results.
aT = sum(algorithmTimer)./((numSamples/fs)*numel(noiseConditions)); for ik = 1:numel(algorithms) fprintf('- %s : %0.3f (s)\n',algorithms{ik},aT(ik)) end
- NCF : 0.022 (s) - PEF : 0.074 (s) - CEP : 0.022 (s) - LHS : 0.030 (s) - SRH : 0.065 (s)
Input Arguments
audioIn
— Audio input signal
vector | matrix
Audio input signal, specified as a vector or matrix. The columns of the matrix are treated as individual audio channels.
Data Types: single
| double
fs
— Sample rate (Hz)
positive scalar
Sample rate of the input signal in Hz, specified as a positive scalar.
The sample rate must be greater than or equal to twice the upper bound of
the search range. Specify the search range using the
Range
name-value pair.
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: pitch(audioIn,fs,Range=[50,150],Method="PEF")
Range
— Search range for pitch estimates
[50,400]
(default) | two-element row vector with increasing positive integer
values
Search range for pitch estimates, specified as a two-element row
vector with increasing positive integer values. The function searches
for a best estimate of the fundamental frequency within the upper and
lower band edges specified by the vector, according to the algorithm
specified by Method
. The range is inclusive and
units are in Hz.
Valid values for the search range depend on the sample rate,
fs
, and on the values of
WindowLength
and
Method
:
Method | Minimum Range | Maximum Range |
---|---|---|
"NCF " |
| Range(2) <
|
"PEF " | 10 < Range(1) | Range(2) <
min(4000, |
"CEP " |
| Range(2) <
|
"LHS " | 1 < Range(1) | Range(2) < |
"SRH " | 1 < Range(1) | Range(2) < |
Data Types: single
| double
WindowLength
— Number of samples in analysis window
round(fs
*0.052)
(default) | integer
fs
*0.052)Number of samples in the analysis window, specified as an integer in
the range [1, min(size(audioIn
,1), 192000)].
Typical analysis windows are in the range 20–100 ms. The default window
length is 52 ms.
Data Types: single
| double
OverlapLength
— Number of samples of overlap between adjacent analysis windows
round(fs
*0.042)
(default) | integer
fs
*0.042)Number of samples of overlap between adjacent analysis windows,
specified as an integer in the range
(-inf
,WindowLength
). A
negative overlap length indicates non-overlapping analysis
windows.
Data Types: single
| double
Method
— Method used to estimate pitch
"NCF"
(default) | "PEF"
| "CEP"
| "LHS"
| "SRH"
Method used to estimate pitch, specified as "NCF"
,
"PEF"
,"CEP"
,
"LHS"
, or "SRH"
. The different
methods of calculating pitch provide trade-offs in terms of noise
robustness, accuracy, and computation expense. The algorithms used to
calculate pitch are based on the following papers:
Data Types: char
| string
MedianFilterLength
— Median filter length used to smooth pitch estimates over time
1
(default) | positive integer
Median filter length used to smooth pitch estimates over time,
specified as a positive integer. The default, 1
,
corresponds to no median filtering. Median filtering is a postprocessing
technique used to remove outliers while estimating pitch. The function
uses movmedian
after
estimating the pitch using the specified
Method
.
Data Types: single
| double
Output Arguments
f0
— Estimated fundamental frequency (Hz)
scalar | vector | matrix
Estimated fundamental frequency, in Hz, returned as a scalar, vector, or
matrix. The number of rows returned depends on the values of the
WindowLength
and OverlapLength
name-value pairs, and on the input signal size. The number of columns
(channels) returned depends on the number of columns of the input signal
size.
Data Types: single
| double
loc
— Locations associated with fundamental frequency estimations
scalar | vector | matrix
Locations associated with fundamental frequency estimations, returned as a
scalar, vector, or matrix the same size as f0
.
Fundamental frequency is estimated locally over a region of
WindowLength
samples. The values of
loc
correspond to the most recent sample (largest
sample number) used to estimate fundamental frequency.
Data Types: single
| double
Algorithms
The pitch
function segments the audio input according to the
WindowLength
and OverlapLength
arguments.
The fundamental frequency is estimated for each frame. The locations output,
loc
contains the most recent samples (largest sample numbers)
of the corresponding frame.
For a description of the algorithms used to estimate the fundamental frequency, consult the corresponding references:
References
[1] Atal, B.S. "Automatic Speaker Recognition Based on Pitch Contours." The Journal of the Acoustical Society of America. Vol. 52, No. 6B, 1972, pp. 1687–1697.
[2] Gonzalez, Sira, and Mike Brookes. "A Pitch Estimation Filter robust to high levels of noise (PEFAC)." 19th European Signal Processing Conference. Barcelona, 2011, pp. 451–455.
[3] Noll, Michael A. "Cepstrum Pitch Determination." The Journal of the Acoustical Society of America. Vol. 31, No. 2, 1967, pp. 293–309.
[4] Hermes, Dik J. "Measurement of Pitch by Subharmonic Summation." The Journal of the Acoustical Society of America. Vol. 83, No. 1, 1988, pp. 257–264.
[5] Drugman, Thomas, and Abeer Alwan. "Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics." Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 2011, pp. 1973–1976.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2018a
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