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资源说明：Automatic Music Performance Analysis and Comparison Toolkit
Example Usage
- from the included script ensembleScript.m
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% exampleScript.m
%
% Description:
% Example of how to use the HMM alignment algorithm
%
% Automatic Music Performance Analysis and Analysis Toolkit (AMPACT)
% http://www.ampact.org
% (c) copyright 2011 Johanna Devaney (j@devaney.ca), all rights reserved.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% audio file to be aligned
audiofile=('example.wav');
% MIDI file to be aligned
midifile=('example.mid');
% number of notes to align
numNotes=6;
% vector of order of states (according to lyrics) in stateOrd and
% corresponding note numbers in noteNum
% 1 indicates a rest at the beginning of ending of the note
% 2 indicates a transient at the beginning or ending of the note
% 3 indicates a steady state section
% the following encoding is for six syllables "A-ve Ma-ri-(i)-a"
% syllable A-ve Ma-ri-(i)-a
% state type 13 23 23 23 3 31
% note number 11 22 33 44 5 66
stateOrd = [1 3 2 3 2 3 2 3 3 3 1];
noteNum = [1 1 2 2 3 3 4 4 5 6 6];
% load singing means and covariances for the HMM alignment
load SingingMeansCovars.mat
means=sqrtmeans;
covars=sqrtcovars;
% specify that the means and covariances in the HMM won't be learned
learnparams=0;
% run the alignment
[allstate selectstate,spec,yinres]=runAlignment(audiofile, midifile, numNotes, stateOrd, noteNum, means, covars, learnparams);
% visualise the alignment
alignmentVisualiser(selectstate,midifile,spec,1);
% get onset and offset times
times=getOnsOffs(selectstate);
% write the onset and offset times to an audacity-readable file
dlmwrite('example.txt',[times.ons' times.offs'], 'delimiter', '\t');
% you can load 'example.txt' into audacity and correct any errors in the
% alignment, i.e., the offset error on the last note, and then reload the
% corrected labels into matlab
fixedLabels=load('exampleFixed.txt');
times.ons=fixedLabels(:,1)';
times.offs=fixedLabels(:,2)';
% map timing information to the quantized MIDI file
nmatNew=getTimingData(midifile, times);
writemidi(nmatNew,'examplePerformance.mid')
% get cent values for each note
cents=getCentVals(times,yinres);
% calculate intervals size, perceived pitch, vibrato rate, and vibrato depth
[vibratoDepth, vibratoRate, intervalSize, perceivedPitch]=getPitchVibratoData(cents,yinres.sr);
% get loudness values for each note using the Genesis Loudness Toolbox
[loudnessEstimates loudnessStructure]=getLoudnessEstimates(audiofile, times);
% get DCT values for each note
for i = 1 : length(cents)
% find the peaks and troughs in the F0 trace for each note
[mins{i} maxes{i}] = findPeaks(cents{i}, 100, yinres.sr/32, 60);
% find the midpoints between mins and maxes in the F0 trace for each
% note
[x_mids{i} y_mids{i}] = findMids(cents{i}, mins{i}, maxes{i}, 100, yinres.sr/32);
% generate a smoothed trajectory of a note by connecting the
% midpoints between peaks and troughs.
smoothedF0s{i}=smoothNote(cents{i}, x_mids{i}, y_mids{i});
% find the steady-state portion of a note
steady{i}(1:2)=findSteady(cents{i}, mins{i}, maxes{i}, x_mids{i}, y_mids{i}, 1);
% compute the DCT of a signal and approximate it with the first 3 coefficients
[dctVals{i}, approx{i}]=noteDct(smoothedF0s{i}(steady{i}(1):steady{i}(2)),3,yinres.sr/32);
end
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AMPACT Function Descriptions
runAlignment.m: Calls the DTW alignment function and refines the results with the HMM alignment algorithm, with both a basic and modified state spaces (based on the lyrics).
getVals.m: Gets values for DTW alignment and YIN analysis of specified audio signal and MIDI file
runDTWAlignment.m: Performs a dynamic time warping alignment between specified audio and MIDI files.
runHMMAlignment.m: Refines DTW alignment values with a three-state HMM, identifying silence,transient, and steady state parts of the signal. The HMM uses the DTW alignment as a prior.
filltransmat.m: Makes a transition matrix from a seed transition matrix.
fillpriormat_gauss.m: Creates a prior matrix based on the DTW alignment (supplied by the input variables ons and offs).
selectStates.m: Refines the HMM parameters according to the modified state sequence vector passed into the function.
alignmentVisualiser.m: Plots a gross DTW alignment overlaid with the fine alignment resulting from the HMM aligner on the output of YIN.
getTimingData: Create a note matrix with performance timings.
getCentVals: Get cent values (in relation to A, 440 Hz) for each note.
getPitchVibratoData: Calculate vibrato depth, vibrato rate, perceived pitch, and interval size for the notes in the inputted cell array cents.
getLoudnessEstimates: Get loudness estimate based on Glasberg and Moore (2002) for time-varying sounds using the Loudness Toolbox.
findPeaks: Find peaks and troughs in a signal.
findMids: Find the midpoints between mins and maxes in a signal.
smoothNote: Generate a smoothed trajectory of a note by connecting the midpoints between peaks and troughs.
noteDct: Compute the DCT of a signal and approximate it with a specified number of coefficients.
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AMPACT Dependencies
You will need to have the following toolkits installed and in your path
de Cheveigné, A. 2002. YIN MATLAB implementation Available from: http://audition.ens.fr/adc/sw/yin.zip
Ellis, D. P. W. 2003. Dynamic Time Warp (DTW) in Matlab. Available from: http://www.ee.columbia.edu/~dpwe/resources/matlab/dtw/
Ellis, D. P. W. 2008. Aligning MIDI scores to music audio. Available from: http://www.ee.columbia.edu/~dpwe/resources/matlab/alignmidiwav/
Genesis Acoustics. 2010. Loudness Toolbox for Matlab. Available from http://www.genesis-acoustics.com/index.php?page=32
Murphy, K. 1998. Hidden Markov Model (HMM) Toolbox for Matlab. Available from http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html
Toiviainen, P. and T. Eerola. 2006. MIDI Toolbox. Available from: https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/miditoolbox/
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Papers on algorithms developed for AMPACT
Devaney, J., M. I. Mandel, and I. Fujinaga. 2011. Characterizing Singing Voice Fundamental Frequency Trajectories. Proceedings of the 2011 Workshop on Applications of Signal Processing to Audio and Acoustics.
Devaney, J., M. I. Mandel, D. P. W. Ellis, and I. Fujinaga. 2010. Automatically extracting performance data from recordings of trained singers. Psychomusicology: Music, Mind & Brain. 21(1–2): in press.
Devaney, J. 2011. An empirical study of the influence of musical context on intonation practices in solo singers and SATB ensembles. Ph. D. Dissertation. McGill University.
Devaney, J., M. I. Mandel, and D. P. W. Ellis. 2009. Improving MIDI-audio alignment with acoutics features. In Proceedings of the 2009 Workshop on Applications of Signal Processing to Audio and Acoustics.
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Papers on algorithms by other authors used by AMPACT
de Cheveigné, A., and H. Kawahara. 2002. YIN, a fundamental frequency estimator for speech and music. Journal of the Acoustical Society of America 111 (4): 1917–30.
Orio, N., and D. Schwarz. 2001. Alignment of monophonic and polyphonic music to a score. In Proceedings of the International Computer Music Conference, 155–8.
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