The content-based retrieval of Western music has received increasing attention recently. Much of this research deals with monophonic music. Polyphonic music is more common, but also more difficult to represent. Music information retrieval systems must extract viable features before they can define similarity measures. We summarize and categorize representation features that have been used for polyphonic retrieval with the aim of laying standardized groundwork for future feature extraction research. Comparisons with and extensions to monophonic approaches are given, and a new feature, an extension of duration-independent pitch slices, is proposed. Key words: Music retrieval, polyphonic features 1.

The emergence of digital music on the Internet requires new information retrieval methods adapted to the specific characteristics and needs. While music retrieval based on the text information, such as title, composers, or subject classification, has been implemented in many existing systems, retrieval of a piece of music based on music contents, especially based on an incomplete, imperfect recall of a fragment of the music, has not yet been fully explored. This thesis is to explore the main problems involved in a web-based melody retrieval system. I propose to build a query-by-humming system, which can find a piece of music in the digital music repository based on a few hummed notes, using a melody representation that combines both the pitch contour and the beat information. Since an input query (hummed melody) may have various errors due to uncertainty of the user's memory or the user's singing ability, the system should be able to tolerate the errors. Furthermore, extracting m...

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In recent years, the searching and indexing techniques for multimedia data are getting more attention in the area of multimedia databases. As many research works were done on the content-based retrieval of image and video data, less attention was received to the content-based retrieval of audio data. In this paper, we propose an approach to retrieve MP3 music objects based on their content. In our approach, the coefficients extracting from the output of the polyphase filters are used to compute the MP3 features for indexing the MP3 objects. We also propose an MP3 similarity measuring function to provide users the ability to approximately retrieve the desired MP3 objects. Experiments are performed and analyzed to show the efficiency and the effectiveness of the proposed method.

This paper describes a method for automatically discovering themes in music. A program based on this algorithm can generate a theme index from a music database

As there is a growing amount of MP3 music data available on the Internet today, the problems related to music classification and content-based music retrieval are getting more attention recently. In this paper, we propose an approach to automatically classify MP3 music objects according to their singers. First, the coefficients extracted from the output of the polyphase filters are used to compute the MP3 features for segmentation. Based on these features, an MP3 music object can be decomposed into a sequence of notes (or phonemes). Then for each MP3 phoneme in the training set, its MP3 feature is extracted and used to train an MP3 classifier which can identify the singer of an unknown MP3 music object. Experiments are performed and analyzed to show the effectiveness of the proposed method.

The support for audio in existing hypermedia systems is generally not as comprehensive as for text and images, considering audio to be an endpoint medium. Temporal linking has been considered in the Soundviewer for Microcosm. This linking model is beginning to become more mainstream through the development of standards associated with the World Wide Web, such as SMIL. This thesis investigates the application of content based navigation to music. It considers the viability of using melodic pitch contours as the content representation for retrieval and navigation. It observes the similarity between content based retrieval and navigation and focuses on the fast retrieval of music. The technique of using n-grams for efficient and accurate content based retrieval is explored. The classification of MIDI tracks into musical roles is used to reduce the amount of data that must be indexed, and so increase speed and accuracy. The impact of classification is evaluated against a melodic retrieval engine. Finally, the system built for retrieval is modified for navigation. A set of tools which support both temporal and content based navigation is built to provide proof of concept.

Extraction of repetitive patterns of the main melody in a given music piece is investigated in this research. A dictionarybased approach is proposed to achieve the task. The input to the proposed system is a piece of music consisting of numerical music scores (e.g. the MIDI file format), and other music forms such as the sound wave have to be converted to numerical music scores first. In the system, segmentation is done based on the tempo information and a music score is decomposed into bars. Each bar is indexed, and a bar index table is built accordingly. Then, an adaptive dictionary-based algorithm known as the Lempel Ziv 78 (LZ-78) is modified and applied to the bar-represented music scores to extract repetitive patterns. The LZ78 algorithm is slightly modified to achieve better results, and the modified LZ78 is named the “Exhaustive Search with Progressive LEngth ” (ESPLE). After this step, pruning is applied to this dictionary to remove non-repeating patterns. Modified LZ78 and pruning are repetitively applied to the updated dictionary, which is generated from the previous cycle, until the dictionary converges. Experiments are performed on MIDI files to demonstrate the superior performance of the proposed algorithm.

This paper presents a genre classification algorithm for symbolic music data. The proposed methodology relies on note pitch and duration features, derived from the repeating patterns and duration histograms of a musical piece, respectively. Note-information histograms have a great capability in capturing a fair amount of information regarding harmonic as well as rhythmic features of different musical genres and pieces, while repeating patterns refer to segments of the piece that are semantically important. Detailed experimental results on intra-classical genres illustrate the significant performance gains due to the proposed features.

This paper describes a method of automatically creating a content-based index of musical scores. The goal is to capture the themes, or motifs, that appear in the music. The method was tested by building an index of 25 orchestral movements from the classical music literature. For every movement, the system captured the primary theme, or a variation of the primary theme. In addition, it captured 13 of 28 secondary themes. The resulting index was 14 % of the size of the database. A further reduction of 2 % is possible; however, this discards secondary themes. A listening experiment using five orchestral movements showed that people can reliably recognize secondary themes after listening to a piece of music–therefore, it may be necessary to retain secondary themes in a score index. 1.