A huge amount of audio data is accessible to everyone by on-line or off-line information services and it is necessary to develop techniques to automatically describe and deal with this data in a meaningful way. In the particular context of music content processing it is important to take into account the melodic aspects of the sound. The goal of this article is to review the different techniques proposed for melodic description and extraction. Some ideas around the concept of melody are first presented. Then, an overview of the different ways of describing melody is done. As a third step, an analysis of the methods proposed for melody extraction is made, including pitch detection algorithms. Finally, techniques for melodic pattern induction and matching are also studied, and some useful melodic transformations are reviewed. 1

A software system that automatically classifies MIDI files into hierarchically organized taxonomies of musical genres is presented. This extensible software includes an easy to use and flexible GUI. An extensive library of high-level musical features is compiled, including many original features. A novel hybrid classification system is used that makes use of hierarchical, flat and round robin classification. Both k-nearest neighbour and neural network-based classifiers are used, and feature selection and weighting are performed using genetic algorithms. A thorough review of previous research in automatic genre classification is presented, along with an overview of automatic feature selection and classification techniques. Also included is a discussion of the theoretical issues relating to musical genre, including but not limited to what mechanisms humans use to classify music by genre and how realistic genre taxonomies can be constructed.

The problem of assigning appropriate pitch spellings is one of the most fundamental problems in the analysis of digital music information. We present an algorithm for finding the optimal spelling based on the Spiral Array model, a geometric model embodying the relations in tonality. The algorithm does not require the key context to be determined. Instead, it uses a center of effect (c.e.), an interior point in the Spiral Arraymodel,asaproxy for the key context. Plausible pitch spellings are measured against this c.e., and the optimal pitch is selected using the nearest neighbor criteria. Two examples are given from Beethoven's Sonata Op. 109 to illustrate the algorithm. The algorithm is implemented and the results used in MuSA ; amusic visualization software using the Spiral Array.We present and analyze computational results from test runs on MIDI files of twomovements from Beethoven's Piano Sonatas Op.79 and Op.109.

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This paper is concerned with the handling of rhythm in music content processing applications. Keeping this framework in mind, we briefly report on terminology issues and the interdisciplinary nature of rhythm investigations, we then review approaches to computational modeling of rhythm and rhythm representation schemes. We comment the bottomup and top-down oriented approaches to computational modeling and the parallel that some authors make with physiological and cognitive views on rhythm perception. We argue that investigations should be listener-oriented, signal-oriented and application-oriented. 1

This paper presents a history of robotic musical instruments that are performed by motors, solenoids, and gears. Automatic mechanical musical instruments from pianos, to turntables, to percussion, to plucked and bowed strings to wind and horns are presented. Quotes from interviews with a number of artists, engineers, and scientists who have built robotic instruments are included. Personal motivations, skill required for building musical robots, as well as future directions of the field of study are also discussed. 1.

Implicit predictions made by kinematic models.............................................................................. 6 Note density.......................................................................................................................................... 8 Method............................................................................................................................................ 10 Results ............................................................................................................................................. 11 Rhythmic structure ............................................................................................................................ 12 Method............................................................................................................................................ 15 Results ............................................................................................................................................. 15 Global tempo...................................................................................................................................... 17 Conclusion .......................................................................................................................................... 18 References ........................................................................................................................................... 20 3 ABSTRACT The relation between music and motion has been a topic of much theoretical and empirical research. An important contribution is made by a family of ...

We synchronize background music with an animation by changing the timing of both, an approach which minimizes the damage to either. Starting from a MIDI file and motion data, feature points are extracted from both sources, paired, and then synchronized using dynamic programming to time-scale the music and to timewarp the motion. We also introduce the music graph, a directed graph which encapsulates connections between many short music sequences. By traversing a music graph we can generate large amounts of new background music, in which we expect to find a sequence which matches the motion better than the original music. Categories and Subject Descriptors (according to ACM CCS): J.5 [Computer Applications]: Arts And Humanities 1.

Machine learning techniques such as classification have proven to be vital tools in both music information retrieval and music performance, where they are useful for leveraging data to learn and model relationships between low-level features and high-level musical concepts. Explicitly supporting feature extraction and classification in a computer music programming language could lower barriers to musicians applying classification in more performance contexts and encourage exploration of music performance as a unique machine learning application domain. Therefore, we have constructed a framework to execute feature extraction and classification in ChucK, forming a foundation for porting MIR solutions into a real-time performance context as well as developing new solutions directly in the language. We describe this work in depth, prefaced by introductions to music information retrieval, machine learning, and the ChucK language. We present three case studies of applying learning in real-time to performance tasks in ChucK, and we propose that fusing learning abilities with ChucK’s real-time, on-the-fly aesthetic suggests exciting new ways of using and interacting with learning algorithms in live computer music performance. 1.

this report. This thesis will describe attempts to build a real-time interactive system for computer music which allows a human instrumentalist to establish the rhythmic frame of performance. The computer must search for evidence of metrical hierarchy or timeline from the audio signal, tracking tactus (beat induction) whilst allowing for expressive timing and motor noise, and all this without excessive zeal but within some reasonably smooth trajectory