A Turkish makam music symbolic database for music information retrieval - SymbTr (original) (raw)
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A Corpus for Computational Research of Turkish Makam Music
2014
Each music tradition has its own characteristics in terms of melodic, rhythmic and timbral properties as well as semantic understandings. To analyse, discover and explore these culture-specific characteristics, we need music collections which are representative of the studied aspects of the music tradition. For Turkish makam music, there are various resources available such as audio recordings, music scores, lyrics and editorial metadata. However, most of these resources are not typically suited for computational analysis, are hard to access, do not have su cient quality or do not include adequate descriptive information. In this paper we present a corpus of Turkish makam music created within the scope of the CompMusic project. The corpus is intended for computational research and the primary considerations during the creation of the corpus reflect some criteria, namely, purpose, coverage, completeness, quality and re-usability. So far, we have gathered approximately 6000 audio recordings, 2200 music scores with lyrics and 27000 instances of editorial metadata related to Turkish makam music. The metadata include information about makams, recordings, scores, compositions , artists etc. as well as the interrelations between them. In this paper, we also present several test datasets of Turkish makam music. Test datasets contain manual annotations by experts and they provide ground truth for specific computational tasks to test, calibrate and improve the research tools. We hope that this research corpus and the test datasets will facilitate academic studies in several fields such as music information retrieval and computational musicology.
A symbolic dataset of Turkish makam music phrases
One of the basic needs for computational studies of traditional music is the availability of free datasets. This study presents a large machine-readable dataset of Turkish makam music scores segmented into phrases by experts of this music. The segmentation facilitates computational research on melodic similarity between phrases, and relation between melodic phrasing and meter, rarely studied topics due to unavailability of data resources.
A Hierarchical Approach to Makam Classification of Turkish Makam Music, Using Symbolic Data
A method for hierarchical classification of makams from symbolic data is presented. A makam generally implies a miscellany of rules for melodic composition using a given scale. Therefore, makam detection is to some level similar to the key detection problem. The proposed algorithm classifies makams by applying music theoretical knowledge and statistical evidence in a hierarchical manner.The makams using similar scales are first grouped together, and then identified in detail later. The first level of the hierarchical decision is based on statistical information provided by the n-gram likelihood of the symbolic sequences.Across-entropy based metric, perplexity, is used to calculate similarity between makam models and the input music piece. Later, using statistical features related to the content of the piece, such as the tonic note, the average pitch level for local excerpts and the overall pitch progression, a more detailed identification of the makam is achieved. Different length n-grams and representation paradigms are used, including the Arel theory, the 12 tone equal tempered representation, and interval contour. Results show that the hierarchical approach is better, compared to a straightforward n-gram classification, for the makamswhich have similar pitch space, such as Hüseyni–Muhayyer and Rast–Mahur. Using the proposed methodology, the system’s recall rate increases from 88.7% to 90.9% where there exists still some confusion between the makams Uşşak and Beyati.
Linking Scores and Audio Recordings in Makam Music of Turkey
Journal of New Music Research, 2014
The most relevant representations of music are notations and audio recordings, each of which emphasizes a particular perspective and promotes different approximations in the analysis and understanding of music. Linking these two representations and analyzing them jointly should help to better study many musical facets by being able to combine complementary analysis methodologies. In order to develop accurate linking methods, we have to take into account the specificities of a given type of music. In this paper, we present a method for linking musically relevant sections in a score of a piece from makam music of Turkey (MMT) to the corresponding time intervals of an audio recording of the same piece. The method starts by extracting relevant features from the score and from the audio recording. The features of a given score section are compared with the features of the audio recording to find the candidate links in the audio for that score section. Next, using the sequential section information stored in the score, it selects the most likely links. The method is tested on a dataset consisting of instrumental and vocal compositions of MMT, achieving 92.1% and 96.9% F1-scores on the instrumental and vocal pieces, respectively. Our results show the importance of culture-specific and knowledge-based approaches in music information processing.
A musical information retrieval system for Classical Turkish Music makams
SAGE Journals, 2017
Musical information retrieval (MIR) applications have become an interesting topic both for researchers and commercial applications. The majority of the current knowledge on MIR is based on Western music. However, traditional genres, such as Classical Turkish Music (CTM), have great structural differences compared with Western music. Then, the validity of the current knowledge on this subject must be checked on such genres. Through this work, a MIR application that simulates the human music processing system based on CTM is proposed. To achieve this goal, first mel-frequency ceps-tral coefficients (MFCCs) and delta-MFCCs, which are the most frequent features used in audio applications, were used as features. In the last few years deep belief networks (DBNs) have become promising classifiers for sound classification problems. To confirm this statement, the classification accuracies of four probability theory-based neural networks, namely radial basis function networks, generalized regression neural networks, probabilistic neural networks, and support vector machines, were compared to the DBN. Our results show that the DBN outperforms the others.
Automatic transcription of Turkish makam music
In this paper we propose an automatic system for transcribing makam music of Turkey. We document the specific traits of this music that deviate from properties that were targeted by transcription tools so far and we compile a dataset of makam recordings along with aligned microtonal ground-truth. An existing multi-pitch detection algorithm is adapted for transcribing music in 20 cent resolution, and the final transcription is centered around the tonic frequency of the recording. Evaluation metrics for transcribing microtonal music are utilized and results show that transcription of Turkish makam music in e.g. an interactive transcription software is feasible using the current state-of-the-art.
The lack of a proper database has been a major obstacle for the analysis of Non-Western music. To pave the way for research on Eastern music and particularly on Persian music, we have decided to create a database for the Persian music on the Santur instrument. This database consists of single notes and melodies from two Santurs, a 9bridge and an 11-bridge. The database will be used to construct and analyse the Dastgàh, which are the Persian music scales. This database is tailored to music processing tasks such as note and scale recognition and identification of the melody. Furthermore, it will assist in the analysis of acoustical properties of the Santur. This paper describes its construction, as well as initial analysis results.
Classification of Classic Turkish Music Makams
In this work, Classical Turkish Music songs are classified into six makams. Makam is a modal framework for melodic development in Classical Turkish Music. The effect of the sound clip length on the system performance was also evaluated. The Mel Frequency Cepstral Coefficients (MFCC) were used as features. Obtained data were classified by using Probabilistic Neural Network. The best correct recognition ratio was obtained as 89,4% by using a clip length of 6 s.