Compression-based Modelling of Musical Similarity Perception

Similarity is an important concept in music cognition research since the similarity between (parts of) musical pieces determines perception of stylistic categories and structural relationships between parts of musical works. The purpose of the present research is to develop and test models of musical similarity perception inspired by a transformational approach which conceives of similarity between two perceptual objects in terms of the complexity of the cognitive operations required to transform the representation of the first object into that of the second, a process which has been formulated in informationtheoretic terms. Specifically, computational simulations are developed based on compression distance in which a probabilistic model is trained on one piece of music and then used to predict, or compress, the notes in a second piece. The more predictable the second piece according to the model, the more efficiently it can be encoded and the greater the similarity between the two pieces. The present research extends an existing information-theoretic model of auditory expectation (IDyOM) to compute compression distances varying in symmetry and normalisation using high-level symbolic features representing aspects of pitch and rhythmic structure. Comparing these compression distances with listeners’ similarity ratings between pairs of melodies collected in three experiments demonstrates that the compression-based model provides a good fit to the data and allows the identification of representations, model parameters and compression-based metrics that best account for musical similarity perception. The compression-based model also shows comparable performance to the best-performing algorithms on the MIREX 2005 melodic similarity task

A computational study on outliers in world music

The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country

Some vantage indexing approaches for rhythmic and melodic search

In this paper, we discuss several improvements and approaches to musical content-based search that utilize the vantage indexing technique. We show how one can build an optimum vantage index for a variant of the Earth Mover s Distance that is appropriate for searching rhythmic patterns. We discuss a genetic algorithm approach for finding good vantage objects for arbitrary distance measures that obey the triangle inequality. Finally, we discuss a method which utilizes vantage indexing for searching prametric spaces spaces in which the distance measure does not obey the triangle inequality and where distinct points may have distance 0

Music Retrieval based on Melodic Similarity

This thesis introduces a method for measuring melodic similarity for notated music such as MIDI files. This music search algorithm views music as sets of notes that are represented as weighted points in the two-dimensional space of time and pitch. Two point sets can be compared by calculating how much effort it would take to convert one into the other; effort is measured by determining how much weight has to be moved over what distances. To make these point set comparisons efficient enough for searching large databases, the distances between every item (point set) in the database and a small, ?xed set of special (vantage) point sets can be pre-calculated. Whenever a new query needs to be compared to the items in the database, one can restrict the search to those items with similar distances to the special point sets. For studying the performance of the transportation-based search algorithm and other, similar ones, the creation of a ground truth for a large music collection (RISM) is described, along with a performance measure and the application of both the ground truth and the measure for the MIREX algorithm competition