Chroma binary similarity and local alignment applied to cover song identification

We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the Music Information Retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms.This article first presents a series of experiments carried outwith two state-of-the-art methods for cover song identification.We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or Dynamic Time Warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best-performing ones are finally applied to the newly proposed method. Multipleevaluations of this one confirm a large increase in identificationaccuracy when comparing it with alternative state-of-the-artapproaches.

"¡Mezquita No!: the origins of mosque opposition in Spain

This paper examines why mosque opposition has been more frequent in Catalonia than in other Spanish regions. A comparison is conducted between the metropolitan areas of Barcelona, where opposition has been most prevalent, and Madrid, where it has been strikingly absent. A relational approach is employed to highlight the factors in Barcelona that have complicated the reception of mosques and the populations they serve. These factors include pronounced socio-spatial divisions and a lack of confidence in the state's commitment to managing the challenges that accompany immigration. The prevalence of these factors in Barcelona has resulted in the integration of mosque debates into more general struggles over urban privilege and state recognition, explaining the high degree of opposition. These findings highlight the importance of studying conflicts related to religious and cultural diversification in context, as such conflicts are inextricably linked to the lived spaces and local structures in which they develop.

Opposition to Anti-Racism Laws across Swiss Municipalities: A Multilevel Analysis

Public opposition to antiracism laws-an expression of prejudice toward immigrants-is widespread in Switzerland as well as in other European countries. Using data from the European Social Survey 2002 (N = 1,711), the present study examined across Swiss municipalities individual and contextual predictors of opposition to such laws and of two well-established antecedents of prejudice: perceived threat and intergroup contact. The study extends multilevel research on immigration attitudes by investigating the role of the ideological climate prevailing in municipalities (conservative vs. progressive), in addition to structural features of municipalities. Controlling for individual-level determinants, stronger opposition to antiracism laws was found in more conservative municipalities, while the proportion of immigrants was positively related to intergroup contact. Furthermore, in conservative municipalities with a low proportion of immigrants, fewer intergroup contacts were reported. In line with prior research, intergroup contact decreased prejudiced policy stances through a reduction of perceived threat. Overall, this study highlights the need to include normative and ideological features of local contexts in the analysis of public reactions toward immigrants.

Simple models for multi-attribute choice with many alternatives: When it does and does not pay to face tradeoffs with binary attributes

Working Paper no longer available. Please contact the author.

Take-the-best and other simple strategies: Why and when they work 'well' in binary choice

The effectiveness of decision rules depends on characteristics of bothrules and environments. A theoretical analysis of environments specifiesthe relative predictive accuracies of the lexicographic rule 'take-the-best'(TTB) and other simple strategies for binary choice. We identify threefactors: how the environment weights variables; characteristics of choicesets; and error. For cases involving from three to five binary cues, TTBis effective across many environments. However, hybrids of equal weights(EW) and TTB models are more effective as environments become morecompensatory. In the presence of error, TTB and similar models do not predictmuch better than a naïve model that exploits dominance. We emphasizepsychological implications and the need for more complete theories of theenvironment that include the role of error.

Cumulative dominance and heuristic performance in binary multi-attribute choice

Several studies have reported high performance of simple decision heuristics multi-attribute decision making. In this paper, we focus on situations where attributes are binary and analyze the performance of Deterministic-Elimination-By-Aspects (DEBA) and similar decision heuristics. We consider non-increasing weights and two probabilistic models for the attribute values: one where attribute values are independent Bernoulli randomvariables; the other one where they are binary random variables with inter-attribute positive correlations. Using these models, we show that good performance of DEBA is explained by the presence of cumulative as opposed to simple dominance. We therefore introduce the concepts of cumulative dominance compliance and fully cumulative dominance compliance and show that DEBA satisfies those properties. We derive a lower bound with which cumulative dominance compliant heuristics will choose a best alternative and show that, even with many attributes, this is not small. We also derive an upper bound for the expected loss of fully cumulative compliance heuristics and show that this is moderateeven when the number of attributes is large. Both bounds are independent of the values ofthe weights.

Implementing TURF analysis through binary linear programming

This paper introduces the approach of using Total Unduplicated Reach and Frequency analysis (TURF) to design a product line through a binary linear programming model. This improves the efficiency of the search for the solution to the problem compared to the algorithms that have been used to date. The results obtained through our exact algorithm are presented, and this method shows to be extremely efficient both in obtaining optimal solutions and in computing time for very large instances of the problem at hand. Furthermore, the proposed technique enables the model to be improved in order to overcome the main drawbacks presented by TURF analysis in practice.

France électorale divisée en arrondissem[en]ts ministériels et de l'opposition / par Vello Geogra[p]he

Échelle(s) : [ca 1:2 607 000]

Ignoring information in binary choice with continuous variables: When is less 'more'?

When can a single variable be more accurate in binary choice than multiple sources of information? We derive analytically the probability that a single variable (SV) will correctly predict one of two choices when both criterion and predictor are continuous variables. We further provide analogous derivations for multiple regression (MR) and equal weighting (EW) and specify the conditions under which the models differ in expected predictive ability. Key factors include variability in cue validities, intercorrelation between predictors, and the ratio of predictors to observations in MR. Theory and simulations are used to illustrate the differential effects of these factors. Results directly address why and when one-reason decision making can be more effective than analyses that use more information. We thus provide analytical backing to intriguing empirical results that, to date, have lacked theoretical justification. There are predictable conditions for which one should expect less to be more.