Representation and discovery of feature set patterns in music

In this paper we describe and apply a method for discovering patterns within music corpora. Previous approaches to the problem have represented melodies in terms of single abstract attribute sequences, and therefore efficient repeated substring algorithms could be applied for the detection of recurrent patterns. This paper presents a new approach, based on sequential pattern mining and knowledge representation methods for music, that permits data mining of music event sequences in which events are represented as multiple feature sets. A feature set represents a conjunction of features held by an event, and can refer to any number of rhythmic and melodic attributes. A depth-first maximal frequent sequence pattern mining algorithm was implemented and applied to melodies of French singer-songwriter Georges Brassens. Results illustrate the feasibility of extending music pattern discovery methods with a more expressive pattern representation. Melodic Contour Extraction for Indian Classical Vocal Music Ashutosh Bapat, Vishweshwara Rao, and Preeti Rao Abstract. The problem of pitch tracking of the singing voice in the presence of Indian percussive interference, specifically the tabla, is considered. To overcome the problems due to this particular type of interference, a pitch tracker is used that applies dynamic programming (DP) based smoothing on pitch estimates obtained from a spectral-domain pitch detection algorithm (PDA) that uses harmonic matching. Experiments on real and simulated signals show the superiority of the spectral domain PDA over a correlation domain PDA in terms of pitch detection accuracy and suitability of the PDA output for post-processing. A new smoothing cost function is proposed and evaluated. The paper formulates general rules guiding the choice of cost functions participating in the DP based post-processing for this particular problem. The problem of pitch tracking of the singing voice in the presence of Indian percussive interference, specifically the tabla, is considered. To overcome the problems due to this particular type of interference, a pitch tracker is used that applies dynamic programming (DP) based smoothing on pitch estimates obtained from a spectral-domain pitch detection algorithm (PDA) that uses harmonic matching. Experiments on real and simulated signals show the superiority of the spectral domain PDA over a correlation domain PDA in terms of pitch detection accuracy and suitability of the PDA output for post-processing. A new smoothing cost function is proposed and evaluated. The paper formulates general rules guiding the choice of cost functions participating in the DP based post-processing for this particular problem. A cooperative approach to style-oriented music composition D. Espi, P. J. Ponce de Leon, C. Perez-Sancho, D. Rizo, J. M. Inesta, F. Moreno-Seco, and A. Pertusa Abstract. Evolutionary methods have been largely used in algorithmic music composition due to their ability to explore an immense space of possibilities. The main problem of genetic related composition algorithms has always been the implementation of the selection process. In this work, a pattern recognition-based system helped by a number of music analysis rules is designed for that task. The fitness value provided by this kind of supervisor (the music critic) models the affect for a certain music genre after a training phase. The early stages of this work have been encouraging since they have responded to the a priori expectations and more work has to be carried out in the future to explore the creative capabilities of the proposed system. Evolutionary methods have been largely used in algorithmic music composition due to their ability to explore an immense space of possibilities. The main problem of genetic related composition algorithms has always been the implementation of the selection process. In this work, a pattern recognition-based system helped by a number of music analysis rules is designed for that task. The fitness value provided by this kind of supervisor (the music critic) models the affect for a certain music genre after a training phase. The early stages of this work have been encouraging since they have responded to the a priori expectations and more work has to be carried out in the future to explore the creative capabilities of the proposed system. A Complexity-based Approach to Melody Track Identification in MIDI Files Søren Tjagvad Madsen and Gerhard Widmer Abstract. In this paper, we will examine the importance of music complexity as a factor for melody recognition in multi-voiced popular music. The assumption is that the melody (or lead instrument) will contain the largest amount of information – that it will be the least redundant voice. Measures of melodic complexity calculated from pitch and timing information are proposed. We test the different complexity measures and different prediction strategies, and evaluate them on the task of predicting which track of a MIDI file contains the main melody. Filtering out melody tracks can be useful when searching large databases for similar songs. 108 melody track annotated pop songs were included in the experiment. In this paper, we will examine the importance of music complexity as a factor for melody recognition in multi-voiced popular music. The assumption is that the melody (or lead instrument) will contain the largest amount of information – that it will be the least redundant voice. Measures of melodic complexity calculated from pitch and timing information are proposed. We test the different complexity measures and different prediction strategies, and evaluate them on the task of predicting which track of a MIDI file contains the main melody. Filtering out melody tracks can be useful when searching large databases for similar songs. 108 melody track annotated pop songs were included in the experiment. A Genetic Rule-based Expressive Performance Model for Jazz Saxophone Rafael Ramirez and Amaury Hazan Abstract. In this paper, we describe an evolutionary approach to inducing a generative model of expressive music performance for Jazz saxophone. We begin with a collection of audio recordings of real Jazz saxophone performances from which we extract a symbolic representation of the musician’s expressive performance. We then apply an evolutionary algorithm to the symbolic representation in order to obtain computational models for different aspects of expressive performance. Finally, we use these models to automatically synthesize performances with the expressiveness that characterizes the music generated by a professional saxophonist. This work extends our previous work on inducing rules using genetic algorithms for In this paper, we describe an evolutionary approach to inducing a generative model of expressive music performance for Jazz saxophone. We begin with a collection of audio recordings of real Jazz saxophone performances from which we extract a symbolic representation of the musician’s expressive performance. We then apply an evolutionary algorithm to the symbolic representation in order to obtain computational models for different aspects of expressive performance. Finally, we use these models to automatically synthesize performances with the expressiveness that characterizes the music generated by a professional saxophonist. This work extends our previous work on inducing rules using genetic algorithms for understanding expressive music performance. An Experiment on Evolutionary Essentic Sound Soh Igarashi and Koichi Furukawa Abstract. This paper describes an experiment on evolutionarily generated essentic sound as a new proposal for composing evolutionary music. Essentic sound originally proposed by Dr. Manfred Clynes is the sound that expresses several emotions human beings typically have. This work utilized genetic algorithm for synthesizing essentic sound and tried to generate such sound material as expresses an emotion gradually rising from some chaotic sound. The algorithmic details are given and its aesthetic intention is discussed. The significance and potentiality of this work is also discussed. Some examples of the synthesized sound can be obtained through the author’s web page. This paper describes an experiment on evolutionarily generated essentic sound as a new proposal for composing evolutionary music. Essentic sound originally proposed by Dr. Manfred Clynes is the sound that expresses several emotions human beings typically have. This work utilized genetic algorithm for synthesizing essentic sound and tried to generate such sound material as expresses an emotion gradually rising from some chaotic sound. The algorithmic details are given and its aesthetic intention is discussed. The significance and potentiality of this work is also discussed. Some examples of the synthesized sound can be obtained through the author’s web page. Using Multiple-Part Learning and a Genetic Algorithm to Compose Emotion-Inducing Tunes Roberto Legaspi, Yuya Hashimoto, Koichi Moriyama, Satoshi Kurihara, Masayuki Numao Abstract. This work aims to computationally specify a system’s music compositional intelligence that tightly couples with the listener’s affective perceptions. The system initially learns first-order rules from multiple-part data to model a listener’s emotional impressions of music using musical score fragments that were hand-labeled by the listener according to a semantic differential scale that uses six affective adjective pairs. A genetic algorithm, whose fitness function is based on the acquired rules and follows specific music theory, is then used to generate variants of frame and chord structures. Lastly, the system integrates melody to the GA-obtained structures. Empirical results show that the system is 80.6% accurate at the average in classifying the affective labels of musical structures and is able to automatically generate musical pieces that stimulate four kinds of impressions, namely, favorable-unfavor