Case Based Representation and Retrieval with Time Dependent Features

Abstract. The temporal dimension of the knowledge embedded in cases has often been neglected or oversimplified in Case Based Reasoning sys-tems. However, in several real world problems a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, if some features describe parameters varying within a period of time (which corresponds to the case duration), and are therefore collected in the form of time series; (2) at the history level, if the evolution of the system can be reconstructed by retrieving temporally related cases. In this paper, we describe a framework for case representation and retrieval able to take into account the temporal dimension, and meant to be used in any time dependent domain. In particular, to support case retrieval, we provide an analysis of similarity-based time series retrieval techniques; to support history retrieval, we introduce possible ways to summarize the case content, together with the corresponding strategies for identifying similar instances in the knowledge base. A concrete ap-plication of our framework is represented by the system RHENE, which is briefly sketched here, and extensively described in [20].

Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

IDENTIFICATION OF COVER SONGS USING INFORMATION THEORETIC MEASURES OF SIMILARITY

13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted versio

k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier -- classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data. This paper is the second edition of a paper previously published as a technical report. Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN

Modeling Temporal Structure in Music for Emotion Prediction using Pairwise Comparisons

The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level cognitive or semantic aspects of music such as emotions, genre, and similarity. This paper addresses the specific hypothesis whether temporal information is essential for predicting expressed emotions in music, as a prototypical example of a cognitive aspect of music. We propose to test this hypothesis using a novel processing pipeline: 1) Extracting audio features for each track resulting in a multivariate "feature time series". 2) Using generative models to represent these time series (acquiring a complete track representation). Specifically, we explore the Gaussian Mixture model, Vector Quantization, Autoregressive model, Markov and Hidden Markov models. 3) Utilizing the generative models in a discriminative setting by selecting the Probability Product Kernel as the natural kernel for all considered track representations. We evaluate the representations using a kernel based model specifically extended to support the robust two-alternative forced choice self-report paradigm, used for eliciting expressed emotions in music. The methods are evaluated using two data sets and show increased predictive performance using temporal information, thus supporting the overall hypothesis

Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure