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Beat tracking systems for music recordings
Staňková, Karolína ; Miklánek, Štěpán (referee) ; Ištvánek, Matěj (advisor)
This master thesis deals with systems for detecting rhythmic structures of music recordings. The field of Music Information Retrieval (MIR) allows us to examine the harmonic and tonal properties of music, rhythm, tempo, etc., and has uses in academic and commercial sphere. Various algorithms are used in the detection of rhythmic structures. However, today, most new methods use neural networks. This work aims to summarize the current research results of systems for detecting beats and tempo, to describe methods of calculating and evaluating the parameters of music recordings, and to implement a program that allows comparison of available detection systems. The result of the work is a script in the Python language, which uses six different systems to detect the rhythmic structure of test recordings. It then checks the outputs of the algorithms according to the given reference and compares the given systems with each other using several evaluation values. It uses two datasets as a reference—one of them is publicly available and the other was created by the author of this thesis (including annotations, i.e., reference beat times, for the sample recordings). The program allows user to see the results in graphs and play any of the sample recordings with detected beat times.
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Tempo detector based on a neural network
Suchánek, Tomáš ; Smékal, Zdeněk (referee) ; Ištvánek, Matěj (advisor)
This Master’s thesis deals with beat tracking systems, whose functionality is based on neural networks. It describes the structure of these systems and how the signal is processed in their individual blocks. Emphasis is then placed on recurrent and temporal convolutional networks, which by they nature can effectively detect tempo and beats in audio recordings. The selected methods, network architectures and their modifications are then implemented within a comprehensive detection system, which is further tested and evaluated through a cross-validation process on a genre-diverse data-set. The results show that the system, with proposed temporal convolutional network architecture, produces comparable results with foreign publications. For example, within the SMC dataset, it proved to be the most successful, on the contrary, in the case of other datasets it was slightly below the accuracy of state-of-the-art systems. In addition,the proposed network retains low computational complexity despite increased number of internal parameters.
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Extraction of parameters for the research of music performance
Laborová, Anna ; Miklánek, Štěpán (referee) ; Ištvánek, Matěj (advisor)
Different music performances of the same piece may significantly differ from each other. Not only the composer and the score defines the listener’s music experience, but the music performance itself is an integral part of this experience. Four parameter classes can be used to describe a performance objectively: tempo and timing, loudness (dynamics), timbre, and pitch. Each of the individual parameters or their combination can generate a unique characteristic performance. The extraction of such objective parameters is one of the difficulties in the field of Music Performance Analysis and Music Information Retrieval. The submitted work summarizes knowledge and methods from both of the fields. The system is applied to extract data from 31 string quartet performances of 2. movement Lento of String Quartet no. 12 F major (1893) by czech romantic composer Antonín Dvořák (1841–1904).
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Web interface for audio feature visualization
Putz, Viliam ; Ištvánek, Matěj (referee) ; Miklánek, Štěpán (advisor)
This thesis deals with methods of audio features extraction from audio files, visualization of these features and implementation of web interface, which provides the visualization. In the introduction, Music Information Retrieval field, with which this thesis is closely related, is described. Also, the current state in the area of applications for audio features extraction is described. Next, the most common libraries for audio feature extraction within the programming languages are listed. In the second chapter, the audio features that can be extracted from audio file are listed and described. In the third chapter, there is description of the process of implementation, used technologies, function diagram of the web interface, explanation of functionality and description of user interface and its functions.
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System for finding duplicate recordings based on audio information
Švejcar, Michael ; Miklánek, Štěpán (referee) ; Ištvánek, Matěj (advisor)
This diploma thesis discusses different methods of detecting duplicates in a music file database. The problem at hand is that files containing the same recording may differ in sound quality, applause at the end of a performance and other such parameters. The aim of this thesis is to design and implement a system that identifies duplicate recordings and provides an output file for the comparison. The system needs to not be affected by the mentioned parameters but precise enough to prevent matching non-identical recordings. The system is realized using the Python programming language, freely available libraries for computing chroma features, Image Hashing technique and multiple variants of the dynamic time warping algorithm. Three comparison methods were implemented in the system, differing in precision and computation complexity. The methods were then tested on a prepared dataset and four preset precision options were created. The final system seems very precise and insusceptible to detecting recordings that are very similar but not identical as duplicates, for example in case of different interpretations of the same musical piece.
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Beat Tracking: Is 441 kHz Really Needed?
Ištvánek, Matěj ; Miklánek, Štěpán
Beat tracking is essential in music informationretrieval, with applications ranging from music analysis and automaticplaylist generation to beat-synchronized effects. In recentyears, deep learning methods, usually inspired by well-knownarchitectures, outperformed other beat tracking algorithms. Thecurrent state-of-the-art offline beat tracking systems utilize temporalconvolutional and recurrent networks. Most systems use aninput sampling rate of 44.1 kHz. In this paper, we retrain multipleversions of state-of-the-art temporal convolutional networks withdifferent input sampling rates while keeping the time resolutionby changing the frame size parameter. Furthermore, we evaluateall models using standard metrics. As the main contribution,we show that decreasing the input audio recording samplingfrequency up to 5 kHz preserves most of the accuracy, and insome cases, even slightly outperforms the standard approach.
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Piano chord analyzer
Poloček, Dominik ; Miklánek, Štěpán (referee) ; Ištvánek, Matěj (advisor)
The presented thesis deals with the analysis of chords by determining the frequencies of their components. The aim of thesis is to outline methods for determining the fundamental frequencies of single and multiple notes and to implement a system that can determine chords using these methods. The method, implemented in Python (spectral peak method), uses a fast Fourier transform to represent the signal in the frequency domain and then searches for spectral maxima, which it evaluates as fundamental frequencies after proper checking. The spectral peaks method was compared with the harmonic component modulus summation method and with the state-of-the- art system for transcribing recordings to MIDI (PianoTransctiprion) by running tests on the dataset created for this thesis (530 chord and note recordings). The best results are presented by PianoTranscription ( = 0.74, tot = 0.23), the second best performing method is the spectral peaks method with a known number of tones ( = 0.55, tot = 0.29), followed by the same method with unknown number of tones ( = 0.52, tot = 0.38) and finally the harmonic component modulus summation method ( = 0.26, tot = 0.81). The limitations of the implemented system are the inability to determine the number of tones (must be specified by the user) and the frequency minimum (138.59 Hz), below which the estimates are erroneous, which is probably due to the design of the piano and the braiding of strings.
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Chord structure detection in music recordings
Kučera, Ondřej ; Miklánek, Štěpán (referee) ; Ištvánek, Matěj (advisor)
This thesis deals with music information retrieval, namely automatic chord recognition in audio recordings. The thesis defines the concepts of chord and chroma features and describes the methods of converting the signal from the time domain to the frequency domain. The thesis explores methods for automatic chord detection; the state-of-the-art methods are based on deep learning. The thesis includes a system implemented in Python that allows chord detection from audio recordings. Individual recordings and associated chord labels can be visualized. The system offers a choice of methods for chord recognition – a method based on chord templates, a method using deep chroma vectors, and a method based on a convolutional neural network. The results of the methods are evaluated on a multi-genre dataset compiled from freely available annotations and recordings.
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Analysis of automatic parameter extraction on piano recordings
Kaplan, Josef ; Miklánek, Štěpán (referee) ; Ištvánek, Matěj (advisor)
This bachelor thesis deals with the analysis of the accuracy of automatic extraction of parameters, mainly of piano recordings. The given issue is described both from a technical and a musical perspective. This thesis summarizes knowledge from the field of music theory and the automatic detection of parameters that can be obtained from musical piano recordings. This thesis is focused on detecting onsets, beats, downbeats, pitch estimation and tempo. The analysis of piano recordings is realized using the Python programming language. The output is scripts that perform parameter detection based on user-selected methods that are commonly used to calculate parameters. The result is also testing the accuracy of individual methods based on annotations from different datasets, focusing primarily on piano recordings. The final part contains an evaluation based on selected metrics with an objective comparison.
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Web applications supporting education of signal processing fundamentals
Kuře, Dominik ; Ištvánek, Matěj (referee) ; Rajmic, Pavel (advisor)
The main topic of the thesis is the creation of four web applications which are used as learning material for students who are studying the basics of signal processing. The areas on which the thesis focuses on are root mean square and expected value of signals, basic signal operations (amplification, geometric translation, scale change), the effect these operations have on the Fourier series of the given signal and also resampling of a signal using different methods of interpolation (nearest neighbour method, linear interpolation, cubic interpolation and interpolation using the sinc function). These applications are implemented using the TypeScript programming language which is an extension of the JavaScript language which enhances it with static types. Other libraries that are used are the React library which is used for front-end web applications and a library which allows easy to implement but still very detailed manipulation of charts called Chart.js. The first half of the theoretical part of the thesis focuses on those areas of signal processing which are necessary to understand so the applications can be created. The second half focuses on information technologies used for the implementation of said applications. Besides the already mentioned technologies, the text also briefly mentions the basics of HTML and CSS languages as well as the JSX syntax. The practical part describes how the applications were implemented and also serves as documentation for the source code. This part shows the reader how to create differently shaped signals in code (sine, triangle, sawtooth, square with different duty cycles, noise) and how to obtain the Fourier series of each of these signals, how to implement different signal operations, how to interpolate between multiple points using different interpolation formulas and what are some of the methods which can be used to apply cubic interpolation (finite difference method, cardinal spline, Catmull–Rom spline, natural cubic interpolation), what the applications look like and what is their structure.
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