International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Spiral Array: An Algorithm for Determining Key Boundaries
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
A New Method for Tracking Modulations in Tonal Music in Audio Data Format
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
A Template-Matching Approach to Free-Form Feature Recognition
IV '00 Proceedings of the International Conference on Information Visualisation
The Cognition of Basic Musical Structures
The Cognition of Basic Musical Structures
Tonal Description of Polyphonic Audio for Music Content Processing
INFORMS Journal on Computing
Audio key finding: considerations in system design and case studies on Chopin's 24 preludes
EURASIP Journal on Applied Signal Processing
Pattern Recognition in South Indian Classical Music Using a Hybrid of HMM and DTW
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
IEEE Transactions on Audio, Speech, and Language Processing
Precise pitch profile feature extraction from musical audio for key detection
IEEE Transactions on Multimedia
A query by humming system based on locality sensitive hashing indexes
Signal Processing
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This study reviews the use of pitch histograms in music information retrieval studies for western and non-western music. The problems in applying the pitch-class histogram-based methods developed for western music to non-western music and specifically to Turkish music are discussed in detail. The main problems are the assumptions used to reduce the dimension of the pitch histogram space, such as, mapping to a low and fixed dimensional pitch-class space, the hard-coded use of western music theory, the use of the standard diapason (A4=440Hz), analysis based on tonality and tempered tuning. We argue that it is more appropriate to use higher dimensional pitch-frequency histograms without such assumptions for Turkish music. We show in two applications, automatic tonic detection and makam recognition, that high dimensional pitch-frequency histogram representations can be successfully used in Music Information Retrieval (MIR) applications without such pre-assumptions, using the data-driven models.