Wavelet-filtering of symbolic music representations for folk tune segmentation and classification

The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering. The method is compared with previously proposed Gestaltbased method. Melodies are represented as discrete symbolic pitch-time signals. We apply the continuous wavelet transform (CWT) with the Haar wavelet at specific scales, obtaining filtered versions of melodies emphasizing their information at particular time-scales. We use the filtered signal for representation and segmentation, using the wavelet coefficients ’ local maxima to indicate local boundaries and classify segments by means of k-nearest neighbours based on standard vector-metrics (Euclidean, cityblock), and compare the results to a Gestalt-based segmentation method and metrics applied directly to the pitch signal. We found that the wavelet based segmentation and waveletfiltering of the pitch signal lead to better classification accuracy in cross-validated evaluation when the time-scale and other parameters are optimized. 1

Melodic segmentation: structure, cognition, algorithms

Segmentation of melodies into smaller units (phrases, themes, motifs, etc.) is an important process in both music analysis and music cognition. Also, segmentation is a necessary preprocessing step for various tasks in music information retrieval. Several algorithms for automatic segmentation have been proposed, based on different music-theoretical backgrounds and computing approaches. Rule-based models operate on a given set of logical conditions. Learning-based models, originating in linguistics, compute segmentation criteria on the basis of statistical parameters of a training corpus and/or of the given composition. The aim of this preliminary study is to propose and describe a new segmentation algorithm that is rule-based, parsimonious, and unambiguous

An approach to melodic segmentation and classification based on filtering with the Haar wavelet

We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wavelet, and we evaluate it on two tasks. The filtered signal corresponds to a single-scale signal ws from the continuous Haar wavelet transform. The melodies are first segmented using local maxima or zero-crossings of ws. The segments of ws are then classified using the k–nearest neighbour algorithm with Euclidian and city-block distances. The method proves more effective than using unfiltered pitch signals and Gestalt-based segmentation when used to recognize the parent works of segments from Bach’s Two-Part Inventions (BWV 772–786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the method is comparable to the use of pitch signals, but not as good as that of string-matching methods based on multiple features

Graph based representation of the music symbolic level. A music information retrieval application

In this work, a new music symbolic level representation system is described. It has been tested in two information retrieval tasks concerning similarity between segments of music and genre detection of a given segment. It could include both harmonic and contrapuntal informations. Moreover, a new large dataset consisting of more than 5000 leadsheets is presented, with meta informations taken from different web databases, including author information, year of first performance, lyrics, genre, etc.ope

Melodic segmentation: evaluating the performance of algorithms and musical experts

We review several segmentation algorithms, qualitatively highlighting their strengths and weaknesses. We also provide a detailed quantitative evaluation of two existing approaches, Temperley\u27s Grouper and Cambouropoulos\u27 Local Boundary Detection Model. In order to facilitate the comparison of an algorithm\u27s performance with human behavior, we compiled a corpus of melodic excerpts in different musical styles and collected individual segmentations from 19 musicians. We then empirically assessed the algorithms\u27 performance by observing how well they can predict both the musicians\u27 segmentations and data taken from the Essen folk song collection

Towards a general computational theory of musical structure

The General Computational Theory of Musical Structure (GCTMS) is a theory that may be employed to obtain a structural description (or set of descriptions) of a musical surface. This theory is based on general cognitive and logical principles, is independent of any specific musical style or idiom, and can be applied to any musical surface. The musical work is presented to GCTMS as a sequence of discrete symbolically represented events (e.g. notes) without higher-level structural elements (e.g. articulation marks, timesignature etc.)- although such information may be used to guide the analytic process. The aim of the application of the theory is to reach a structural description of the musical work that may be considered as 'plausible' or 'permissible' by a human music analyst. As styledependent knowledge is not embodied in the general theory, highly sophisticated analyses (similar to those an expert analyst may provide) are not expected. The theory gives, however, higher rating to descriptions that may be considered more reasonable or acceptable by human analysts and lower to descriptions that are less plausible

Data-driven, memory-based computational models of human segmentation of musical melody

When listening to a piece of music, listeners often identify distinct sections or segments within the piece. Music segmentation is recognised as an important process in the abstraction of musical contents and researchers have attempted to explain how listeners perceive and identify the boundaries of these segments.The present study seeks the development of a system that is capable of performing melodic segmentation in an unsupervised way, by learning from non-annotated musical data. Probabilistic learning methods have been widely used to acquire regularities in large sets of data, with many successful applications in language and speech processing. Some of these applications have found their counterparts in music research and have been used for music prediction and generation, music retrieval or music analysis, but seldom to model perceptual and cognitive aspects of music listening.We present some preliminary experiments on melodic segmentation, which highlight the importance of memory and the role of learning in music listening. These experiments have motivated the development of a computational model for melodic segmentation based on a probabilistic learning paradigm.The model uses a Mixed-memory Markov Model to estimate sequence probabilities from pitch and time-based parametric descriptions of melodic data. We follow the assumption that listeners' perception of feature salience in melodies is strongly related to expectation. Moreover, we conjecture that outstanding entropy variations of certain melodic features coincide with segmentation boundaries as indicated by listeners.Model segmentation predictions are compared with results of a listening study on melodic segmentation carried out with real listeners. Overall results show that changes in prediction entropy along the pieces exhibit significant correspondence with the listeners' segmentation boundaries.Although the model relies only on information theoretic principles to make predictions on the location of segmentation boundaries, it was found that most predicted segments can be matched with boundaries of groupings usually attributed to Gestalt rules.These results question previous research supporting a separation between learningbased and innate bottom-up processes of melodic grouping, and suggesting that some of these latter processes can emerge from acquired regularities in melodic data

Towards a multi-layer architecture for multi-modal rendering of expressive actions

International audienceExpressive content has multiple facets that can be conveyed by music, gesture, actions. Different application scenarios can require different metaphors for expressiveness control. In order to meet the requirements for flexible representation, we propose a multi-layer architecture structured into three main levels of abstraction. At the top (user level) there is a semantic description, which is adapted to specific user requirements and conceptualization. At the other end are low-level features that describe parameters strictly related to the rendering model. In between these two extremes, we propose an intermediate layer that provides a description shared by the various high-level representations on one side, and that can be instantiated to the various low-level rendering models on the other side. In order to provide a common representation of different expressive semantics and different modalities, we propose a physically-inspired description specifically suited for expressive actions