طبقه‌بندی دستگاه‌های موسیقی سنتی ایرانی با استفاده از ویژگی‌های مبتنی بر فرکانس گام

در این مقاله، سامانه جدیدی با استفاده از ویژگی های مبتنی بر فرکانس گام (pitch) جهت طبقه بندی دستگاه های موسیقی سنتی ایرانی ارائه شده است. موسیقی سنتی ایرانی از هفت دستگاه اصلی شامل چهارگاه، همایون، ماهور، سه‌ گاه، شور، نوا و راست‌ پنجگاه تشکیل می شود. در این الگوریتم،  ویژگی‌ های ضرایب لاگرانژ لگاریتم فرکانس گام (lcpl)، دسته‌ های شباهت فازی نوع دو (fsst2) و نیز ترکیب آنها جهت طبقه‌ بندی سیگنال ‌های موسیقی به‌کار برده می ‌شود و از ماشین بردار پشتیبان چند‌رده‌ای به منظور طبقه ‌بندی دستگاه های موسیقی ایرانی استفاده می شود. دادگان به‌کاررفته در سامانه جداساز ارائه‌ شده، شامل قطعاتی از تک نوازی های تار علیزاده، استاد مطرح موسیقی ایرانی، است. عملکرد الگوریتم های طبقه بندی پایه و پیشنهادی توسط معیارهای ارزیابیaccuracy، recall، precision،f- measure و mcc  صورت می ‌گیرد. نتایج به‌دست‌آمده نشان می دهند که الگوریتم پیشنهادی نسبت به روش های پایه عملکرد بهتری را بر حسب معیارهای مختلف طبقه‌ بندی در بر دارد.

classification of iranian traditional music dastgahs using features based on pitch frequency

the iranian traditional music is composed of seven majors dastgahs: chahargah, homayoun, mahour, segah, shour, nava, and rast-panjgah. in this paper, a new algorithm for the classification of the iranian traditional music dastgahs based on pitch frequency is proposed. in this algorithm, the features of lagrange coefficients of pitch logarithm (lcpl), fuzzy similarity sets type 2 (fsst2), and their combination are used as the representation of music signals which are fed into the multi-class support vector machine (msvm) as the classifier. the features of lcpl and fsst2 are obtained by applying some modifications on the pitch frequency of the desired music. to compute lcpl, first, the values of pitch frequency are extracted by the praat algorithm. then, after the applying a logarithmic operation, the tracks of pitch frequency are partitioned into smaller segments. the method of feature extraction is based on detecting the trough or valley points of the pitch tracks. in the following, the coordinates of trough points (i.e., the index of pitch frequency and the logarithmic value of the frequency) are considered as each segment boundaries. in the next step, the track between the two boundaries of each segment is approximated by a 6th order lagrange polynomial and the computed polynomial coefficients are considered as a 6-dimensional feature vector. the first step in extracting the fsst2 feature is to compute the pitch frequencies of the input signal by the praat algorithm. the second step involves the classification of music notes. then, the subtractive clustering method is used to eliminate the incorrectly estimated pitch frequencies of the previous step. next, the process of folding notes (i.e., transferring the extracted pitch frequencies into the reference octave band of 220-440 hz) is performed followed by translating the frequency points to the cents with respect to 220 hz. after folding notes in one octave, the mahalanobis distance is applied to recognize which point on the reference octave corresponds to each musical note. these same procedures are conducted for the information pattern (theoretical data) of each dastgah. in the final step, the folded frequency points of the unknown input signal and the information pattern of all dastgahs are transferred to the fuzzy logictype-2 domain and compared to determine a similarity measure which is considered as the extracted feature. the dataset used in the proposed classification algorithm contains the excerpts from solo performances with tar played by alizadeh, the well-known iranian music master. the performances of the baselines and proposed classification algorithms are evaluated by the measures of accuracy, recall, precision, f-measure, and mcc. the results show that the proposed algorithm has a better performance as compared with the baseline methods in terms of different classification criteria.