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Harmonic pitch class profiles (HPCP) is a group of features that a computer program extracts from an audio signal, based on a ''pitch class profile''—a descriptor proposed in the context of a chord recognition system.〔Fujishima, T. ''Realtime chord recognition of musical sound: a system using Common Lisp Music'', ICMC, Beijing, China, 1999, pp. 464–467.〕 HPCP are an enhanced pitch distribution feature that are sequences of feature vectors that describe tonality, measuring the relative intensity of each of the 12 pitch classes of the equal-tempered scale within an analysis frame. It is also called Chroma. By processing musical signals, software can identify HPCP features and use them to estimate the key of a piece,〔Gomez, E. Herrera, P. (2004). ''Estimating The Tonality Of Polyphonic Audio Files: Cognitive Versus Machine Learning Modelling Strategies''. ISMIR 2004 – 5th International Conference on Music Information Retrieval.〕 to measure similarity between two musical pieces (cover version identification)〔Joan Serra, Emilia Gomez, Perfecto Herrera, and Xavier Serra ''Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification'' August, 2008〕 and to classify music in terms of composer, genre or mood. The process is related to time-frequency analysis. In general, chroma features are robust to noise (e.g., ambient noise or percussive sounds), independent of timbre and instrumentation and independent of loudness and dynamics. HPCPs are tuning independent and consider the presence of harmonic frequencies, so that the reference frequency can be different from the standard A 440 Hz. The result of HPCP computation is a 12, 24, or 36-bin octave-independent histogram depending on the desired resolution, representing the relative intensity of each 1, 1/2, or 1/3 of the 12 semitones of the equal tempered scale. ==General HPCP feature extraction procedure== The block diagram of the procedure is shown in Fig.1〔Joan Serra, Emilia Gomez, Perfecto Herrera, and Xavier Serra ''Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification'' August, 2008〕 and is further detailed in.〔Gomez, E. ''Tonal description of polyphonic audio for music content processing''. INFORMS Journal on Computing. Special Cluster on Music Computing. Chew, E., Guest Editor, 2004.〕 The General HPCP feature extraction procedure is summarized as follows: #Input musical signal. #Do spectral analysis to obtain the frequency components of the music signal. #Use Fourier transform to convert the signal into a spectrogram. (The Fourier transform is a type of time-frequency analysis.) #Do frequency filtering. A frequency range of between 100 and 5000 Hz is used. #Do peak detection. Only the local maximum values of the spectrum are considered. #Do reference frequency computation procedure. Estimate the deviation with respect to 440 Hz. #Do Pitch class mapping with respect to the estimated reference frequency. This is a procedure for determining the pitch class value from frequency values. A weighting scheme with cosine function is used. It considers the presence of harmonic frequencies (harmonic summation procedure), taking account a total of 8 harmonics for each frequency. To map the value on a one-third of a semitone, the size of the pitch class distribution vectors must be equal to 36. #Normalize the feature frame by frame dividing through the maximum value to eliminate dependency on global loudness. And then we can get a result HPCP sequence like Fig.2. 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「Harmonic pitch class profiles」の詳細全文を読む スポンサード リンク
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