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Cover Song Identification using Music Harmony Features, Model and Complexity Analysis
Maršík, Ladislav ; Pokorný, Jaroslav (advisor) ; Ge, Mouzhi (referee) ; Łukasik, Ewa (referee)
Title: Cover Song Identification using Music Harmony Features, Model and Complexity Analysis Author: Ladislav Maršík Department: Department of Software Engineering Supervisor: Prof. RNDr. Jaroslav Pokorný, CSc., Department of Software Engineering Abstract: Analysis of digital music and its retrieval based on the audio fe- atures is one of the popular topics within the music information retrieval (MIR) field. Every musical piece has its characteristic harmony structure, but harmony analysis is seldom used for retrieval. Retrieval systems that do not focus on similarities in harmony progressions may consider two versions of the same song different, even though they differ only in instrumentation or a singing voice. This thesis takes various paths in exploring, how music harmony can be used in MIR, and in particular, the cover song identification (CSI) task. We first create a music harmony model based on the knowledge of music theory. We define novel concepts: a harmonic complexity of a mu- sical piece, as well as the chord and chroma distance features. We show how these concepts can be used for retrieval, complexity analysis, and how they compare with the state-of-the-art of music harmony modeling. An extensive comparison of harmony features is then performed, using both the novel fe- atures and the...
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Melody Extraction with Deep Learning
Balhar, Jiří ; Hajič, Jan (advisor) ; Maršík, Ladislav (referee)
Melody extraction is arguably one of the most important and challenging problems in Music Information Retrieval. It is melody that we are likely to recall after listening to a song and so it is one of the most relevant aspects of music. However the presence of accompaniment in songs makes the task hard to address using rule-based methods. During the last years data-driven methods based on deep learning started to outperform methods traditionally used in the field. In this thesis we continue in these efforts and propose three new methods for melody extraction. Among these an architecture called Harmonic Convolutional Neural Network, based on a modification of convolutional neural networks to better capture harmonically related information in an input spectrogram with logarithmic frequency axis, was able to achieve state-of-the-art performance on several publicly available melody datasets. 1
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Feature Evaluation for Scalable Cover Song Identification Using Machine Learning
Martišek, Petr ; Maršík, Ladislav (advisor) ; Hajič, Jan (referee)
Cover song identification is a field of music information retrieval where the task is to determine whether two different audio tracks represent different versions of the same underlying song. Since covers might differ in tempo, key, instrumentation and other characteristics, many clever features have been developed over the years. We perform a rigorous analysis of 32 features used in related works while distinguishing between exact and scalable features. The former are based on a harmonic descriptor time series (typically chroma vectors) and offer better performance at the cost of computation time. The latter have a small constant size and only capture global phenomena in the track, making them fast to compute and suitable for use with large datasets. We then select 7 scalable and 3 exact features to build our own two-level system, with the scalable features used on the first level to prune the dataset and the exact on the second level to refine the results. Two distinct machine learning models are used to combine the scalable resp. exact features. We perform the analysis and the evaluation of our system on the Million Song Dataset. The experiments show the exact features being outperformed by the scalable ones, which lead us to a decision to only use the 7 scalable features in our system. The...
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Generating polyphonic music using neural networks
Židek, Marek ; Hajič, Jan (advisor) ; Maršík, Ladislav (referee)
The aim of this thesis is to explore new ways of generating unique polyphonic music using neural networks. Music generation, either in raw audio waveforms or discretely represented, is very interesting and under a heavy ex- ploration in recent years. This thesis works with midi represented polyphonic classical music for piano as training data. We introduce the problem, show rele- vant neural network architectures and describe our numerous ideas, out of which one idea, our experiment with three versions of skip residual LSTM connections for music composition, we consider a good contribution to the field. In related work, skip-connections were explored mostly for classification tasks, however, our results show a solid improvement for music composition (e.g. 47% of respondents considered our samples real). We also show that skip-connections have rather diverse hyperparameter space for future tuning. Apart from standard automated test set evaluation, which is hard to design and interpret for creativity mimicking models, we also did a complex evaluation through surveys. The evaluation was specifically designed to not only to show results for our samples, but to reveal information about expectancy, preconceptions and influence of personal charac- teristics of the respondents. We consider this a valuable...
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Music composition based on a programming language
Pavlín, Tomáš ; Maršík, Ladislav (advisor) ; Hajič, Jan (referee)
Computer music composition brings a lot of problems which can be solved using a variety of approaches. The existing music composition programs either do not provide enough flexibility to composers or they are considerably complicated for users which do not have technical background. In this thesis, we introduce an intuitive programming language designed for music composition along with an interpreter of this language represented by user-friendly graphical interface. The interface can be utilized for music composition and production even by users without technical and musical skills. The program provides a new approach for music composition and allows an effortless music creation that can be used e.g. in game industry. In addition, the program can be used for musical accompaniment. 1
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Web application for sports leagues
Fajta, Václav ; Maršík, Ladislav (advisor) ; Ježek, Pavel (referee)
Organization of individual league competitions in sport centres brings various difficulties. Players need to have access to their results and standings, organizers besides monitoring the league need to set the rules, evaluate the results and organize tournaments. The aim of this work is development of an application which would make such activity simpler. We bring adjustable system, where it is possible to manage a lot of leagues with different demands under one web application. It handles multiple sports, diverse match systems and specific rules of each league. It is built on ASP.NET platform using Model-View-Controller design pattern. We describe technologies and architecture for web development, and present our application using diagrams and screenshots.
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Melody generation using a genetic algorithm
Helikar, Matouš ; Maršík, Ladislav (advisor) ; Křen, Tomáš (referee)
Music composition, as all other creative activities, requires original inspiration, which can also come from melodies generated by a computer. This thesis describes generation of music tracks represented by tree structures with pluggable modules that create or alter individual musical motives. The trees can subsequently be combined by a crossbreeding algorithm driven by user ratings. This results in music tracks evolving on multiple levels, such as the selected instruments or musical motives, rhythm and overall structure. Appropriate settings of parameters for the generator and constituent modules can then produce varied tracks for inspiration or relaxation. The thesis is accompanied by a complete application using these techniques for music generation and a user study of satisfaction with the resulting tracks. Powered by TCPDF (www.tcpdf.org)
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