Given an image (or video clip, or audio song), how do we automatically assign keywords to it? The general problem is to find correlations across the media in a collection of multimedia objects like video clips, with colors, and/or motion, and/or audio, and/or text scripts. We propose a novel, graph-based approach, "MMG", to discover such cross-modal correlations. Our

Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs 1. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.

A Matlab toolbox implementing music similarity measures for audio is presented. The implemented measures focus on aspects related to timbre and periodicities in the signal. This paper gives an overview of the implemented functions. In particular, the basics of the similarity measures are reviewed and some visualizations are discussed. 1.

With the rising popularity of digital music archives the need for new access methods such as interactive exploration or similarity-based search become significant. In this paper we present the PlaySOM, as well as the PocketSOMPlayer, two novel interfaces allowing to browse a music collection by navigating a map of clustered music tracks and to select regions of interest containing similar tracks for playing. The PlaySOM system is primarily designed to allow interaction via a large-screen device, whereas the PocketSOMPlayer is implemented for mobile devices, supporting both local as well as streamed audio replay. This approach offers content-based organization of music as an alternative to conventional navigation of audio archives, i.e. flat or hierarchical listings of music tracks that are sorted and filtered by meta information.

This paper describes a novel music playback interface, called Musicream, which lets a user unexpectedly come across various musical pieces similar to those liked by the user. With most previous “query-by-example ” interfaces used for similarity-based searching, for the same query and music collection a user will always receive the same list of musical pieces ranked by their similarity and opportunities to encounter unfamiliar musical pieces in the collection are limited. Musicream facilitates active, flexible, and unexpected encounters with musical pieces by providing four functions: the music-disc streaming function which creates a flow of many musical-piece entities (discs) from a (huge) music collection, the similaritybased sticking function which allows a user to easily pick out and listen to similar pieces from the flow, the metaplaylist function which can generate a playlist of playlists (ordered lists of pieces) while editing them with a high degree of freedom, and the time-machine function which automatically records all Musicream activities and allows a user to visit and retrieve a past state as if using a time machine. In our experiments, these functions were used seamlessly to achieve active and creative querying and browsing of music collections, confirming the effectiveness of Musicream.

In order to represent musical content, pitch and timing information is utilized in the majority of existing work in Symbolic Music Information Retrieval (MIR). Symbolic representations such as MIDI allow the easy calculation of such information and its manipulation. In contrast, most of the existing work in Audio MIR uses timbral and beat information, which can be calculated using automatic computer audition techniques. In this paper, Pitch Histograms are defined and proposed as a way to represent the pitch content of music signals both in symbolic and audio form. This representation is evaluated in the context of automatic musical genre classification. A multiple-pitch detection algorithm for polyphonic signals is used to calculate Pitch Histograms for audio signals. In order to evaluate the extent and significance of errors resulting from the automatic multiple-pitch detection, automatic musical genre classification results from symbolic and audio data are compared. The comparison indicates that Pitch Histograms provide valuable information for musical genre classification. The results obtained for both symbolic and audio cases indicate that although pitch errors degrade classification performance for the audio case, Pitch Histograms can be effectively used for classification in both cases. 1.

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music.mcgill.ca This paper presents ACE (Autonomous Classification Engine), a framework for using and optimizing classifiers. Given a set of feature vectors, ACE experiments with a variety of classifiers, classifier parameters, classifier ensembles and dimensionality reduction techniques in order to arrive at a good configuration for the problem at hand. In addition to evaluating classification methodologies in terms of success rates, functionality is also being incorporated into ACE allowing users to specify constraints on training and classification times as well as on the amount of time that ACE has to arrive at a solution. ACE is designed to facilitate classification for those new to pattern recognition as well as provide flexibility for those with more experience. ACE is packaged with audio and MIDI feature extraction software, although it can certainly be used with existing feature extractors. This paper includes a discussion of ways in which existing general-purpose classification software can be adapted to meet the needs of music researchers and shows how these ideas have been implemented in ACE. A standardized XML format for communicating features and other information to classifiers is proposed. A special emphasis is placed on the potential of classifier ensembles, which have remained largely untapped by the MIR community to date. A brief theoretical discussion of ensemble classification is presented in order to promote this powerful approach.

We describe a new sequential learning scheme called “stacked sequential learning”. Stacked sequential learning is a meta-learning algorithm, in which an arbitrary base learner is augmented so as make it aware of the labels of nearby examples. We evaluate the method on several “sequential partitioning problems”, which are characterized by long runs of identical labels. We demonstrate that on these problems, sequential stacking consistently improves the performance of non-sequential base learners; that sequential stacking often improves performance of learners (such as CRFs) that are designed specifically for sequential tasks; and that a sequentially stacked maximum-entropy learner generally outperforms CRFs. 1