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In this paper we present and empirically evaluate a user study using a web search log and eye tracking to measure user behaviour during a query session, that is, a sequence of user queries, results page views and content page views, in order to find a specific piece of information. We evaluate different tasks, in terms of those who found the correct information, and in terms of the query session sequence itself, ordered by SERP (Search Engine Result Page), number and return visits to the results page for the same query. From this we are able to identify a number of different behaviour patterns for successful and unsuccessful users, and different trends in user activity during the query session. We find that a user behaves differently after the first query formulation, when we compare with the second formulation (both queries being for the same information item). The results can be used to improve the user experience in the query session, by recognising when the user is displaying one of the patterns we have found to have a low success rate, and offering contextual help at that point. The results may also contribute to improving the design of the results page.

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Web log data has been the basis for analyzing user query session behavior for a number of years, but it has several important shortcomings. The main one being that we do not really know what the user is doing -- is s/he looking at the screen or doing something else? We have conducted an Eye-Tracking study to analyze user behavior when searching the web and looking for specific information on results and content pages. The goal is to obtain more precise information about the search strategy of the user. Which characteristics make the difference between successful and unsuccessful searches? This research presents results focusing on the number of formulated queries by session, documents clicked, the fixation durations on the documents, and the distribution of the attention in the different areas of the screen, among other aspects.

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In this paper we evaluate the effect of a document sanitization process on a set of information retrieval metrics, in order to measure information loss and risk of disclosure. As an example document set, we use a subset of the Wikileaks Cables, made up of documents relating to five key news items which were revealed by the cables. In order to sanitize the documents we have developed a semi-automatic anonymization process following the guidelines of Executive Order 13526 (2009) of the US Administration, by (i) identifying and anonymizing specific person names and data, and (ii) concept generalization based on WordNet categories, in order to identify words categorized as classified. Finally, we manually revise the text from a contextual point of view to eliminate complete sentences, paragraphs and sections, where necessary. We show that a significant sanitization can be applied, while maintaining the relevance of the documents to the queries corresponding to the five key news items.

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In this study we compare the use of different music representations for retrieving alternative performances of the same musical piece, a task commonly referred to as version identification. Given the audio signal of a song, we compute descriptors representing its melody, bass line and harmonic progression using state-of-the-art algorithms. These descriptors are then employed to retrieve different versions of the same musical piece using a dynamic programming algorithm based on nonlinear time series analysis. First, we evaluate the accuracy obtained using individual descriptors, and then we examine whether performance can be improved by combining these music representations (i.e. descriptor fusion). Our results show that whilst harmony is the most reliable music representation for version identification, the melody and bass line representations also carry useful information for this task. Furthermore, we show that by combining these tonal representations we can increase version detection accuracy. Finally, we demonstrate how the proposed version identification method can be adapted for the task of query-by-humming. We propose a melody-based retrieval approach, and demonstrate how melody representations extracted from recordings of a cappella singing can be successfully used to retrieve the original song from a collection of polyphonic audio. The current limitations of the proposed approach are discussed in the context of version identification and query-by-humming, and possible solutions and future research directions are proposed.

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salamon-mmir12.pdf

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By mining user-generated text content we can obtain music-related information that could not otherwise be extracted from audio signals or symbolic score representations. In this paper we propose a methodology for extracting musically-relevant semantic information from an online discussion forum, rasikas.org, dedicated to the Carnatic music tradition. For that we define a dictionary of relevant terms such as raagas, taalas, performers, composers, and instruments, and create a complex network representation by matching such dictionary against the forum posts. This network representation is used to identify popular terms within the forum, as well as relevant co-occurrences and semantic relationships. This way, for instance, we are able to guess the instrument of a performer with 95% accuracy, to discover the confusion between two raagas with different naming conventions, or to infer semantic relationships regarding lineage or musical influence. This contribution is a first step towards the creation of ontologies for a culture-specific art music tradition.

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355_ISMIR_2012.pdf

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Intonation is an important concept in Carnatic music that is characteristic of a raaga, and intrinsic to the musical expression of a performer. In this paper we approach the description of intonation from a computational perspective, obtaining a compact representation of the pitch track of a recording. First, we extract pitch contours from automatically selected voice segments. Then, we obtain a a pitch histogram of its full pitch-range, normalized by the tonic frequency, from which each prominent peak is automatically labelled and parametrized. We validate such parametrization by considering an explorative classification task: three raagas are disambiguated using the characterization of a single peak (a task that would seriously challenge a more naïve parametrization). Results show consistent improvements for this particular task. Furthermore, we perform a qualitative assessment on a larger collection of raagas, showing the discriminative power of the entire representation. The proposed generic parametrization of the intonation histogram should be useful for musically relevant tasks such as performer and instrument characterization.

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199_ISMIR_2012.pdf

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Content-based approaches to music retrieval are of great relevance as they do not require any kind of manually generated annotations. In this paper, we introduce the concept of structure fingerprints, which are compact descriptors of the musical structure of an audio recording. Given a recorded music performance, structure fingerprints facilitate the retrieval of other performances sharing the same underlying structure. Avoiding any explicit determination of musical structure, our fingerprints can be thought of a probability density function derived from a self-similarity matrix. We show that the proposed fingerprints can be compared using simple Euclidean distances without using any kind of complex warping operations required in previous approaches. Experiments on a collection of Chopin Mazurkas reveal that structure fingerprints facilitate robust and efficient content-based music retrieval. Furthermore, we give a musically informed discussion that also deepens the understanding of the popular Mazurka dataset.

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055_ISMIR_2012.pdf

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Collaborative tagging has emerged as an efficient way to semantically describe online resources shared by a community of users. However, tag descriptions present some drawbacks such as tag scarcity or concept inconsistencies. In these situations, tag recommendation strategies can help users in adding meaningful tags to the resources being described. Freesound is an online audio clip sharing site that uses collaborative tagging to describe a collection of more than 130,000 sound samples. In this paper we propose four algorithm variants for tag recommendation based on tag co-occurrence in the Freesound folksonomy. On the basis of removing a number of tags that have to be later predicted by the algorithms, we find that using ranks instead of raw tag similarities produces statistically significant improvements. Moreover, we show how specific strategies for selecting the appropriate number of tags to be recommended can significantly improve algorithms' performance. These two aspects provide insight into some of the most basic components of tag recommendation systems, and we plan to exploit them in future real-world deployments.

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073_ISMIR_2012.pdf

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Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remain formally unknown. In this contribution we use tools and concepts from statistical physics and complex networks to study and quantify the evolution of western contemporary popular music. In it, we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness. Moreover, we find many of these patterns and metrics to be consistently stable for a period of more than fifty years, thus pointing towards a great degree of conventionalism in this type of music. Nonetheless, we prove important changes or trends. These are related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. The obtained results suggest that our perception of new popular music would be largely rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

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Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remain formally unknown. In this contribution we use tools and concepts from statistical physics and complex networks to study and quantify the evolution of western contemporary popular music. In it, we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness. Moreover, we find many of these patterns and metrics to be consistently stable for a period of more than fifty years, thus pointing towards a great degree of conventionalism in this type of music. Nonetheless, we prove important changes or trends. These are related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. The obtained results suggest that our perception of new popular music would be largely rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

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