Emotion recognition of music objects is one of the promising research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even if we have a benchmark (a training data with ground truth) and employ effective classification algorithms, music emotion recognition remains a challenging problem. Based on our literature review, most of previous work only focuses on music acoustic content without considering the individual difference (i.e., Personalization issue). In addition, the assessment of emotions are usually self-reported. Such kind of self-reported assessment (e.g., Emotion tags) might be inaccurate, and even inconsistent. The electroencephalography (EEG) is a non-invasive brain-machine interface, which utilizes neurophysiological signals from the brain to external machines without surgery. The less-intrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. In this paper, we would like to propose an evidence-based and personalized model for music emotion recognition. In the model construction and training phase, we construct two predictive and generic models (both models are trained by artificial neural network). With having the generic model and the corresponding individual difference, we construct the personalized model H by the projective transformation accordingly. In the testing phase, given a music object, we extract features from music audio content, calculate the vector in the arousal-valence emotion space, and apply the transformation matrix H to determine the personalized emotion vector. To show the effectiveness of our approach, we also perform experiments and obtain promising results.

In this paper, we propose a formal model of playlist generation and define three types of constraints, i.e., parameter-specified constraints, derived constraints, and user-defined constraints. Some constraints can be derived through user behaviour and user interactions. Given a set of constraints, we apply a genetic algorithm to generate a playlist which optimizes the number of matched constraints. We also implement our prototype, and perform experiments to show the feasibility and effectiveness of prototype.

In this paper, we describe the Ultima project which aims to construct a platform for evaluating various approaches of music information retrieval. Two kinds of approaches are adopted in this project. These approaches differ in various aspects, such as representations of music objects, index structures, and approximate query processing strategies. For a fair comparison, we propose a measurement of the retrieval effectiveness by recall-precision curves with a scaling factor adjustment. Finally, the performance study of the retrieval effectiveness based on various factors of these approaches is presented.

A repeating pattern in music data is defined as a sequence of notes which appears more than once in a music object. The themes are a typical kind of repeating patterns. The themes and other nontrivial repeating patterns are important music features which can be used for both content-based retrieval of music data and music data analysis. In this paper, we propose two approaches for fast discovering nontrivial repeating patterns in music objects. In the first approach, we develop a data structure called correlative matrix and its associated algorithms for extracting the repeating patterns. In the second approach, we introduce a string-join operation and a data structure called RP-tree for the same purpose. Experiments are performed to compare these two approaches with others. The results are further analyzed to show the efficiency and the effectiveness of our approaches.

Proposes an approach for the fast discovery of all non-trivial repeating patterns in music objects. A repeating pattern is a sequence of notes which appears more than once in a music object. The longest repeating patterns in music objects are typically their themes. The themes and other non-trivial repeating patterns are important musical features which can be used both for content-based retrieval of music data and for music data analysis. We present a data structure called an RP-tree (repeating pattern tree) and its associated algorithms for the fast extraction of all non-trivial repeating patterns in a music object. Experiments are performed to compare this method with related approaches. The results are further analysed to show the efficiency and effectiveness of our approach.

In this paper, we propose an approach for the extraction of the repeating patterns in music objects. A repeating pattern is a sequence of notes which appears more than once in a music object. It is one of the most important music features which can be used for both content-based retrieval of music data and music data analysis. We propose a data structure called correlative matrix and its associated algorithms for extracting all repeating patterns in a music object. Experiments are also performed and the results are analyzed to show the efficiency and the effectiveness of our approach.