Marek Szczerba

Pitch detection enhancement employing music prediction

Pitch detection methods are widely used for extracting musical data from digital signals. A review of those methods is presented in the paper. Since musical signals may contain noise and distortion, detection results can be erroneous. In this paper a new method employing music prediction to support pitch determination is introduced. This method was developed in order to override disadvantages of standard pitch detection algorithms. The new approach utilizes signal segmentation and pitch prediction based on musical knowledge extraction employing artificial neural networks. Signal segmentation allows for estimating the pitch for a single note as a whole, therefore suppressing errors in transient and decay phases. Pitch prediction helps correcting pitch estimation errors by tracking musical context of the analyzed signal. As it was shown in the experimental results, pitch estimation errors may be reduced by using both signal segmentation and music prediction techniques.

Musical Phrase Representation and Recognition by Means of Neural Networks and Rough Sets

This paper discusses various musical phrase representations that can be used to classify musical phrases with a considerable accuracy. Musical phrase analysis plays an important role in music information retrieval domain. In the paper various representations of a musical phrase are described and analyzed. Also the experiments were designed to facilitate pitch prediction within a musical phrase by means of entropy-coding of music. We used the concept of predictive data coding introduced by Shannon. Encoded music representations, stored in the database, are then used for automatic recognition of musical phrases by means of Neural Networks (NN) and rough sets (RS). A discussion on obtained results is carried out and conclusions are included.