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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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The Application Of Tempo Calculation For Musicological Purposes
Istvanek, Matej
Beat tracking systems capture time positions of beats within digital recordings. Theyare used, for example, in streaming portals, but applications in the musicological analysis are oftenneglected. In this article, two different methods of beat tracking systems are tested—conventionaland the state-of-the-art—on the specific motif of a string quartet music, which is one of the mostcomplex tasks for beat detectors in general. The aim here is to determine which system is better formusicology purposes. This often involves determining not only the position of individual beats andestimating the tempo but also the accuracy of determining their number. Evaluation analysis maybe suitable for comparing the accuracy of detectors, but may not necessarily reflect the requirementsof musicological analysis. The results of selected detectors show that a system based on a recurrentneural network seems to be the most suitable.
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Universal Audio 1176 Compressor
Pavlik, Miroslav ; Ištvánek, Matěj (referee) ; Koton, Jaroslav (advisor)
The aim of this bachelor thesis is to get acquainted with the issue of analog compressor Universal Audio 1176 and verify the functionality and principles of individual block diagrams of this compressor. The bachelor's thesis is carried out for a detailed analysis of individual blocks of compressor 1176. A practical implementation was constructed, to verify the correct functionality of compressor 1176 on measurements, which were performed in the laboratory.
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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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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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Audio signal modelling using neural networks
Pešán, Michele ; Ištvánek, Matěj (referee) ; Miklánek, Štěpán (advisor)
Neuronové sítě vycházející z architektury WaveNet a sítě využívající rekurentní vrstvy jsou v současnosti používány jak pro syntézu lidské řeči, tak pro „black box“ modelování systémů pro úpravu akustického signálu – modulační efekty, nelineární zkreslovače apod. Úkolem studenta bude shrnout dosavadní poznatky o možnostech využití neuronových sítí při modelování akustických signálů. Student dále implementuje některý z modelů neuronových sítí v programovacím jazyce Python a využije jej pro natrénování a následnou simulaci libovolného efektu nebo systému pro úpravu akustického signálu. V rámci semestrální práce vypracujte teoretickou část práce, vytvořte zvukovou databázi pro trénování neuronové sítě a implementujte jednu ze struktur sítí pro modelování zvukového signálu. Neuronové sítě jsou v průběhu posledních let používány stále více, a to víceméně přes celé spektrum vědních oborů. Neuronové sítě založené na architektuře WaveNet a sítě využívající rekurentních vrstev se v současné době používají v celé řadě využití, zahrnující například syntézu lidské řeči, nebo napřklad při metodě "black-box" modelování akustických systémů, které upravují zvukový signál (modulačí efekty, nelineární zkreslovače, apod.). Tato akademická práce si dává za cíl poskytnout úvod do problematiky neuronových sítí, vysvětlit základní pojmy a mechanismy této problematiky. Popsat využití neuronových sítí v modelování akustických systémů a využít těchto poznatků k implementaci neuronových sítí za cílem modelování libovolného efektu nebo zařízení pro úpravu zvukového signálu.
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Enhancement Of Global Tempo Computation In Beat Tracking System Based On Teager-Kaiser Energy Operator
Ištvánek, Matěj
Beat detection systems and onset detections are used in music information retrieval (MIR) research field for the calculation of the global tempo (GT) and beat positions in audio recordings. The aim of this article is to introduce the enhancement of the onset detector and therefore the beat tracking system. The enhancement is based on the Teager-Kaiser energy operator (TKEO), which is used in pre-processing stage before the onset computation. The proposed method is firstly evaluated in terms of ability to estimate GT of a given audio track and then it is tested on the string quartet database. Results suggest that the TKEO could improve accuracy of GT estimation. Proposed beat tracking system could be used for analysis of interpretation changes in string quartet music.
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