- Pitch detection algorithm
A pitch detection algorithm (PDA) is an
algorithmdesigned to estimate the pitch or fundamental frequencyof a quasiperiodicor virtually periodic signal, usually a digital recordingof speech or a musical note or tone. This can be done in the time domainor the frequency domain.
PDAs are used in various contexts (e.g.
phonetics, music information retrieval, speech coding, musical performance systems) and so there may be different demands placed upon the algorithm. There is as yet no single perfect PDA, so a variety of algorithms exist, most falling broadly into the classes given below [D. Gerhard. [http://www.cs.uregina.ca/Research/Techreports/2003-06.pdf Pitch Extraction and Fundamental Frequency: History and Current Techniques] , technical report, Dept. of Computer Science, University of Regina, 2003.] .
In the time domain, a PDA typically estimates the period of the quasiperiodic signal, then inverts that value to give the frequency.
One simple approach would be to measure the distance between
zero crossingpoints of the signal (i.e. the Zero Crossing Rate). However, this does not work well with complex waveforms which are composed of multiple sine waves with differing periods. Nevertheless, there are cases in which zero-crossing can be a useful measure, for example in some speech applications where a single source is assumed. The algorithm's simplicity makes it "cheap" to implement.
More sophisticated approaches compare segments of the signal with other segments offset by a trial period to find a match. AMDF (
average magnitude difference function), ASDF (Average Squared Difference Function), or the similar autocorrelationwork this way. These algorithms can give quite accurate results for highly periodic signals. However, they have false detection problems (often "octave errors"), can sometimes cope badly with noisy signals (depending on the implementation) and - in their basic implementations - do not deal with polyphonic sounds (which involve multiple musical notes of different pitches).
Current time-domain pitch detector algorithms tend to build upon the basic methods referred to above, with additional refinements to bring the performance more in line with a human assessment of pitch. For example, the YIN algorithm [A. de Cheveigné and H. Kawahara. [http://www.ircam.fr/pcm/cheveign/pss/2002_JASA_YIN.pdf YIN, a fundamental frequency estimator for speech and music.] The Journal of the Acoustical Society of America, 111:1917, 2002. doi|10.1121/1.1458024] and the MPM algorithm [P. McLeod and G. Wyvill. [http://csweb.otago.ac.nz/tartini/papers/A_Smarter_Way_to_Find_Pitch.pdf A smarter way to find pitch.] In Proceedings of the International Computer Music Conference (ICMC’05), 2005.] are both based upon autocorrelation.
In the frequency domain, polyphonic detection is possible, usually utilizing the
Fast Fourier Transform(FFT) to convert the signal to a frequency spectrum. This requires more processing power as the desired accuracy increases, although the well-known efficiency of the FFTalgorithm makes it suitably efficient for many purposes.
Popular frequency domain algorithms include: the harmonic product spectrum [http://cnx.org/content/m11714/latest/ Pitch Detection Algorithms] , online resource from
Connexions] ; cepstral analysis and maximum likelihoodwhich attempts to match the frequency domain characteristics to pre-defined frequency maps (useful for detecting pitch of fixed tuning instruments); and the detection of peaks due to harmonic series [Mitre, Adriano; Queiroz, Marcelo; Faria, Régis. [http://www.ime.usp.br/~mqz/Mitre_AESBR2006.pdf Accurate and Efficient Fundamental Frequency Determination from Precise Partial Estimates.] Proceedings of the 4th AES Brazil Conference. 113-118, 2006.] .
To improve on the pitch estimate derived from the discrete Fourier spectrum, techniques such as "spectral reassignment" (phase based) or "Grandke interpolation" (magnitude based) can be used to go beyond the resolution provided by the FFT analysis.
Fundamental frequency of speech
The fundamental frequency of
speechcan vary from 40 Hz for low-pitched male voices to 600 Hz for children or high-pitched female voices cite book |last=Huang |first=Xuedong |coauthors=Alex Acero, Hsiao-Wuen Hon |title=Spoken Language Processing |origyear=2001 |publisher=Prentice Hall PTR |language=English |isbn=0-13-022616-5 |pages=325 ] .
Autocorrelation methods need at least two pitch periods to detect pitch. To detect a fundamental frequency of 40 Hz this means that at least 50 milliseconds (ms) of the speech signal must be analyzed. However, during 50 ms, speech with higher fundamental frequencies may not necessarily have the same fundamental frequency throughout the window.
Linear predictive coding
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