Dastgàh recognition in Iranian music: different features and optimized parameters

In this paper we report on the results of utilizing computational analysis to determine the dastgàh, the mode of music in the Iranian classical art music, using spectrogram and chroma features. We contrast the effectiveness of classifying music using the Manhattan distance and Gaussian Mixture Models (GMM). For our database of Iranian instrumental music played on a santur, using spectrogram and chroma features , we achieved accuracy rates of 90.11% and 80.2% when using Manhattan distance respectively. When using GMM with chroma, the accuracy rate was 89.0%. The effects of altering key parameters were also investigated, varying the amount of the training data and silence, as well as high frequency suppression on the results. The results from this phase of experimentation indicated that a 24 equal temperament was the best tone resolution. While experiments focused on dastgàh, with only minor adjustments the described techniques are applicable to traditional Persian, Kurdish, Turkish, Arabic and Greek music, and therefore suitable to use as a basis for a musicological tool that provides a broader form of cross-cultural audio search

Automatic recognition of Persian musical modes in audio musical signals

This research proposes new approaches for computational identification of Persian musical modes. This involves constructing a database of audio musical files and developing computer algorithms to perform a musical analysis of the samples. Essential features, the spectral average, chroma, and pitch histograms, and the use of symbolic data, are discussed and compared. A tonic detection algorithm is developed to align the feature vectors and to make the mode recognition methods independent of changes in tonality. Subsequently, a geometric distance measure, such as the Manhattan distance, which is preferred, and cross correlation, or a machine learning method (the Gaussian Mixture Models), is used to gauge similarity between a signal and a set of templates that are constructed in the training phase, in which data-driven patterns are made for each dastgàh (Persian mode). The effects of the following parameters are considered and assessed: the amount of training data; the parts of the frequency range to be used for training; down sampling; tone resolution (12-TET, 24-TET, 48-TET and 53-TET); the effect of using overlapping or nonoverlapping frames; and silence and high-energy suppression in pre-processing. The santur (hammered string instrument), which is extensively used in the musical database samples, is described and its physical properties are characterised; the pitch and harmonic deviations characteristic of it are measured; and the inharmonicity factor of the instrument is calculated for the first time. The results are applicable to Persian music and to other closely related musical traditions of the Mediterranean and the Near East. This approach enables content-based analyses of, and content-based searches of, musical archives. Potential applications of this research include: music information retrieval, audio snippet (thumbnailing), music archiving and access to archival content, audio compression and coding, associating of images with audio content, music transcription, music synthesis, music editors, music instruction, automatic music accompaniment, and setting new standards and symbols for musical notation