Melody, bassline and harmony representations for music version identification
Publication Type:
Conference PaperSource:
Int. World Wide Web Conf., Workshop on Advances on Music Information Retrieval (AdMIRe), WWW, Lyon, France, p.887-894 (2012)URL:
http://www2012.wwwconference.org/proceedings/forms/companion.htm#8Abstract:
In this paper we compare the use of different musical representations for the task of version identification (i.e. retrieving alternative performances of the same musical piece). We automatically compute descriptors representing the melody and bass line using a state-of-the-art melody extraction algorithm, and compare them to a harmony-based descriptor. The similarity of descriptor sequences is computed using a dynamic programming algorithm based on nonlinear time series analysis which has been successfully used for version identification with harmony descriptors. After evaluating the accuracy of individual descriptors, we assess whether performance can be improved by descriptor fusion, for which we apply a classification approach, comparing different classification algorithms. We show that both melody and bass line descriptors carry useful information for version identification, and that combining them increases version detection accuracy. Whilst harmony remains the most reliable musical representation for version identification, we demonstrate how in some cases performance can be improved by combining it with melody and bass line descriptions. Finally, we identify some of the limitations of the proposed descriptor fusion approach, and discuss directions for future research.
PDFFile:
Identification of versions of the same musical composition: audio content-based approaches and post-processing steps
Publication Type:
BookSource:
LAP Lambert Academic Publishing, Saarbrücken, Germany (2011)ISBN:
978-3847327851Keywords:
Music; Information Retrieval; Time series; Complex networks; SimilarityAbstract:
This book focuses on the automatic identification of musical piece versions (alternate renditions of the same musical composition like cover songs, live recordings, remixes, etc.). In particular, two core approaches for version identification are proposed: model-free and model-based. Furthermore, the book introduces the use of post-processing strategies to improve the identification of versions in a query-by-example paradigm. Overall, several tools and concepts are employed, including nonlinear signal analysis, complex networks, and time series models. This work brings automatic version identification to an unprecedented stage where high accuracies are achieved and, at the same time, explores promising directions for future research. Although the main steps are guided by the nature of the considered signals (music recordings) and the characteristics of the task at hand (version identification), the methodology of this book can be easily transferred to other contexts and domains.
Measuring Similarity in Description Logics using Refinement Operators
Publication Type:
Conference PaperSource:
Case-Based Reasoning Research and Development: 19th International Conference on Case-Based Reasoning (ICCBR'11), Volume 6880, p.289 - 303 (2011)Keywords:
CBR; Similarity; Description Logics; Refinement GraphAbstract:
Similarity assessment is a key operation in many artificial intelligence fields, such as case-based reasoning, instance-based learning, ontology matching, clustering, etc. This paper presents a novel measure for assessing similarity between individuals represented using Description Logic (DL). We will show how the ideas of {\em refinement operators} and {\em refinement graph}, originally introduced for inductive logic programming, can be used for assessing similarity in DL and also for abstracting away from the specific DL being used. Specifically, similarity of two individuals is assessed by first computing their {\em most specific concepts}, then the {\em least common subsumer} of these two concepts, and finally measuring their distances in the refinement graph
PDFFile:
Similarity Measures over Refinement Graphs
Publication Type:
Journal ArticleSource:
Machine Learning, Volume 87, Issue 1, p.57-92 (2012)Keywords:
CBR; Similarity; Machine Learning; Feature TermsAbstract:
Similarity assessment plays a key role in lazy learning methods such as k-nearest neighbor or case-based reasoning. In this paper we will show how refinement graphs, that were originally introduced for inductive learning, can be employed to assess and reason about similarity. We will define and analyze two similarity measures, $S_{\lambda}$ and $S_{\pi}$, based on refinement graphs. The \emph{anti-unification-based similarity}, $S_{\lambda}$, assesses similarity by finding the anti-unification of two instances, which is a description capturing all the information common to these two instances. The \emph{property-based similarity}, $S_{\pi}$, is based on a process of disintegrating the instances into a set of {\em properties}, and then analyzing these property sets.
Moreover these similarity measures are applicable to any representation language for which a refinement graph that satisfies the requirements we identify can be defined. Specifically, we present a refinement graph for feature terms, in which several languages of increasing expressiveness can be defined. The similarity measures are empirically evaluated on relational data sets belonging to languages of different expressiveness.
Interpolation of fuzzy data: Analytical approach and overview
Publication Type:
Journal ArticleSource:
Fuzzy Sets and Systems, Elsevier, Volume 192, p.134-158 (2012)Keywords:
Fuzzy function; Fuzzy space; Similarity; Interpolation of fuzzy data; Interpolating fuzzy functionAbstract:
We propose a general framework for the interpolation problem. Our framework stems from the classical elaboration of the problem. We introduce the notion of an interpolating fuzzy function and show how this function can be characterized. We examine and analyze previously published fuzzy interpolation algorithms to choose those algorithms that can be represented analytically. We also propose an analytic solution of the interpolation problem that unifies various algorithmic approaches.
Similarity for attribute-value representations in Fuzzy Description Logics
Publication Type:
Conference PaperSource:
Artificial Intelligence Research and Development, CCIA'10, IOS Press, p.269-278 (2010)Keywords:
Similarity; Fuzzy; Description LogicsAbstract:
In this paper we explore the possibility of introducing the
equality symbol in the languages of Fuzzy Description Logics (FDLs)
interpreted as a similarity relation. The goal is twofold: dealing with
attribute-value representations at the domain objects level, and integrating
the treatment of similarities inside the description languages and
their corresponding knowledge bases.
On Similarity Measures based on a Refinement Lattice
Publication Type:
Conference PaperSource:
ICCBR'09: 8th International Conference on Case-Based Reasoning, Lecture Notes in Artificial Intelligence, Springer Verlag, Volume 5650, Seattle, p.240-255 (2009)ISBN:
978-3-642-02997-4Keywords:
CBR; similarity; feature logicsAbstract:
Retrieval of structured cases using similarity has been studied in CBR but there has been less activity on defining similarity on description logics (DL). In this paper we present an approach that allows us to present two similarity measures for feature logics, a subfamily of DLs, based on the concept of refinement lattice.
The first one is based on computing the anti-unification (AU) of two cases to assess the amount of shared information. The second measure decomposes the cases into a set of independent properties, and then assesses how many of these properties are shared between the two cases. Moreover, we show that the defined measures are applicable to any representation language for which a refinement lattice can be defined. We empirically evaluate our measures comparing them to other measures in the literature in a variety of relational data sets showing very good results.
