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A pitch detection algorithm (PDA) is an algorithm designed to estimate the pitch or fundamental frequency of a quasiperiodic or virtually periodic signal, usually a digital recording of speech or a musical note or tone. This can be done in the time domain or the frequency domain or both the two domains. 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 ideal PDA, so a variety of algorithms exist, most falling broadly into the classes given below.〔D. Gerhard. (Pitch Extraction and Fundamental Frequency: History and Current Techniques ), technical report, Dept. of Computer Science, University of Regina, 2003.〕 ==Time-domain approaches== In the time domain, a PDA typically estimates the period of a quasiperiodic signal, then inverts that value to give the frequency. One simple approach would be to measure the distance between zero crossing points 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, e.g. 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), ASMDF (Average Squared Mean Difference Function), and other similar autocorrelation algorithms work 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 well with polyphonic sounds (which involve multiple musical notes of different pitches). Current time-domain pitch detector algorithms tend to build upon the basic methods mentioned 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. (YIN, a fundamental frequency estimator for speech and music. ) The Journal of the Acoustical Society of America, 111:1917, 2002. 〕 and the MPM algorithm〔P. McLeod and G. Wyvill. (A smarter way to find pitch. ) In Proceedings of the International Computer Music Conference (ICMC’05), 2005.〕 are both based upon autocorrelation. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Pitch detection algorithm」の詳細全文を読む スポンサード リンク
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