Від шляхетства до дворянства: до історії польської присутності на Ніжинщині в ХVІІ – першій третині ХІХ століття (за матеріалами формулярних списків)

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Corpus Analysis Tools for Computational Hook Discovery.

Compared to studies with symbolic music data, advances in music description from audio have overwhelmingly focused on ground truth reconstruction and maximizing prediction accuracy, with only a small fraction of studies using audio description to gain insight into musical data. We present a strategy for the corpus analysis of audio data that is optimized for interpretable results. The approach brings two previously unexplored concepts to the audio domain: audio bigram distributions, and the use of corpus-relative or 'second-order' descriptors. To test the real-world applicability of our method, we present an experiment in which we model song recognition data collected in a widely-played music game. By using the proposed corpus analysis pipeline we are able to present a cognitively adequate analysis that allows a model interpretation in terms of the listening history and experience of our participants. We find that our corpus-based audio features are able to explain a comparable amount of variance to symbolic features for this task when used alone and that they can supplement symbolic features profitably when the two types of features are used in tandem. Finally, we highlight new insights into what makes music recognizable

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

Music Information Retrieval Using Biologically Inspired Techniques

The computational modeling of our perception of music similarity is an intricate, unsolved problem with various practical applications. Many of the current approaches aim at solving it by employing heuristics, such as expert intuition or music theory, which limit their application to narrow contexts, e.g., certain types of music, certain music representations. This dissertation stems from the observation that biological sequences and music items (melodies, chords sequences) share a number of resembling concepts, from the way they are represented, the way they are transformed through time, to the problems most prominent in their respective fields of study (bioinformatics and music information retrieval). Bioinformatics however, has a long history of algorithm development that offers data-driven instead of heuristic-based solutions. Naturally, such solutions can be adapted to the general task of modeling music similarity. More specifically, this dissertation considers the tasks of melodic and chord sequence similarity, intra-family similarity (the specific similarity among related music items), outlier detection (identifying the musical items that do not belong in a group) and polyphony reconstruction (arranging temporally-corrupted polyphonic voices to their original state). At the same time, this work provides a reliable insight into the mechanics of similarity perception by performing a data-driven analysis on the essential concept of music stability. As such, we consider this dissertation to be a fine balance between the occasionally contradicting goals of music information retrieval, problem-solving and knowledge acquisition

Music Information Retrieval Using Biologically Inspired Techniques

The computational modeling of our perception of music similarity is an intricate, unsolved problem with various practical applications. Many of the current approaches aim at solving it by employing heuristics, such as expert intuition or music theory, which limit their application to narrow contexts, e.g., certain types of music, certain music representations. This dissertation stems from the observation that biological sequences and music items (melodies, chords sequences) share a number of resembling concepts, from the way they are represented, the way they are transformed through time, to the problems most prominent in their respective fields of study (bioinformatics and music information retrieval). Bioinformatics however, has a long history of algorithm development that offers data-driven instead of heuristic-based solutions. Naturally, such solutions can be adapted to the general task of modeling music similarity. More specifically, this dissertation considers the tasks of melodic and chord sequence similarity, intra-family similarity (the specific similarity among related music items), outlier detection (identifying the musical items that do not belong in a group) and polyphony reconstruction (arranging temporally-corrupted polyphonic voices to their original state). At the same time, this work provides a reliable insight into the mechanics of similarity perception by performing a data-driven analysis on the essential concept of music stability. As such, we consider this dissertation to be a fine balance between the occasionally contradicting goals of music information retrieval, problem-solving and knowledge acquisition

The Cover Song Variation Dataset

As digital music collections grow larger, music similarity becomes one of the most prominent concepts in the field of Music Information Retrieval (MIR). Modelling similarity between music pieces allows efficient retrieval and organizing of such collections. Studies have shown that the concept of variation is closely related to similarity, since listeners tend to cluster together musical patterns that are repeated, transformed but still recognizable. Subsequently, musical pieces or segments that contain such patterns are considered similar. Such structural variations are notably present in oral-transmission processes. Folk songs are a standing example of such a process, capturing a huge amount of varying patterns moulded through time. Variations in cover songs in western popular music are also very interesting examples, since a) they can be considered products of a “modern” oral-transmission procedure and b) covers themselves are typically well documented with rich metadata