Intonation is a fundamental music concept that has a special relevance in Indian art music. It is characteristic of a raga and key to the musical expression of the artist. In this paper, we first assess raga intonation qualitatively by analysing varas, a particular form of Carnatic music compositions. We then approach the task of automatically obtaining a compact representation of the intonation of a recording from its pitch track. We propose two approaches based on the parametrization of pitch-value distributions: performance pitch histograms, and context-based svara distributions obtained by categorizing pitch contours based on the melodic context. We evaluate both approaches on a large Carnatic music collection and discuss their merits and limitations. We finally go through different kinds of contextual information that can be obtained to further improve the two approaches. G. K. Koduri, V. Ishwar, J. Serrà, and X. Serra. Intonation analysis of ragas in Carnatic music. Journal of New Music Research, Special Issue on Computational Approaches to the Art Music Traditions of India and Turkey 43(1): 72-93. Mar 2014. Unsupervised music structure annotation by time series structure features and segment similarity17/3/2014
Automatically inferring the structural properties of raw multimedia documents is essential in today’s digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework. J. Serrà, M. Müller, P. Grosche, and J. Ll. Arcos. Unsupervised music structure annotation by time series structure features and segment similarity. IEEE Trans. on Multimedia, Special Issue on Music Data Mining. In press. Note that this is the most complete report of the system that achieved very good results at the MIREX 2012 Structural Segmentation task and that we provide the binaries for the system here. Collaborative tagging has emerged as a common solution for labelling and organising online digital content. However, collaborative tagging systems typically suffer from a number of issues such as tag scarcity or ambiguous labelling. As a result, the organisation and browsing of tagged content is far from being optimal. In this work we present a general scheme for building a folksonomy-based tag recommendation system to help users tagging online content resources. Based on this general scheme, we describe eight tag recommendation methods and extensively evaluate them with data coming from two real-world large-scale datasets of tagged images and sound clips. Our results show that the proposed methods can effectively recommend relevant tags, given a set of input tags and tag co-occurrence information. Moreover, we show how novel strategies for selecting the appropriate number of tags to be recommended can significantly improve methods performances. Approaches such as the one presented here can be useful to obtain more comprehensive and coherent descriptions of tagged resources, thus allowing a better organisation, browsing and reuse of online content. Moreover, they can increase the value of folksonomies as reliable sources for knowledge-mining. F. Font, J. Serrà, and X. Serra. Folksonomy-based tag recommendation for collaborative tagging systems. Int. Journal on Semantic Web and Information Systems 9(2): 1-30. Nov 2013. Our work entitled "Cognitive prognosis of acquired brain injury patients using machine learning techniques", done within the context of the COGNITIO project, received a best paper award from the Int. Conf. on Advanced Cognitive Technologies and Applications (COGNITIVE), 2013.
J. Serrà, J. Ll. Arcos, A. Garcia-Rudolph, A. García-Molina, T. Roig, and J. M. Tormos. Cognitive prognosis of acquired brain injury patients using machine learning techniques. Proc. of the Int. Conf. on Advanced Cognitive Technologies and Applications (COGNITIVE), pp. 108-113. May 2013. A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields. J. Serrà, T. H. Özaslan, J. Ll. Arcos. Note onset deviations as musical piece signatures. PLoS ONE 8(7): e69268. Jul 2013. |
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