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Music mood and emotion recognition using Music information retrieval techniques
Smělý, Pavel ; Mucha, Ján (referee) ; Kiska, Tomáš (advisor)
This work focuses on scientific area called Music Information Retrieval, more precisely it’s subdivision focusing on the recognition of emotions in music called Music Emotion Recognition. The beginning of the work deals with general overview and definition of MER, categorization of individual methods and offers a comprehensive view of this discipline. The thesis also concentrates on the selection and description of suitable parameters for the recognition of emotions, using tools openSMILE and MIRtoolbox. A freely available DEAM database was used to obtain the set of music recordings and their subjective emotional annotations. The practical part deals with the design of a static dimensional regression evaluation system for numerical prediction of musical emotions in music recordings, more precisely their position in the AV emotional space. The thesis publishes and comments on the results obtained by individual analysis of the significance of individual parameters and for the overall analysis of the prediction of the proposed model.
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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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Electric Guitar to MIDI Conversion
Klčo, Michal ; Glembek, Ondřej (referee) ; Černocký, Jan (advisor)
Automatický přepis hudby a odhad vícero znějících tónu jsou stále výzvou v oblasti dolování informací z hudby. Moderní systémy jsou založeny na různých technikách strojového učení pro dosažení co nejpřesnějšího přepisu hudby. Některé z nich jsou také omezeny na konkrétní hudební nástroj nebo hudební žánr, aby se snížila rozmanitost analyzovaného zvuku. V této práci je navrženo, vyhodnoceno a porovnáváno několik systémů pro konverzi nahrávek elektrické kytary do MIDI souború, založených na různých technikách strojového učení a technikách spektrální analýzy.
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Music recommendation based on music information retrieval
Semela, René ; Schimmel, Jiří (referee) ; Kiska, Tomáš (advisor)
This thesis deals with the design, implementation and testing of the content-based music recommender system based on music information retrieval. In the introduction the attention is paid to issues of music information retrieval and to areas of their utilization, it also focuses on tools of their retrieving. Aferwards the most used types of recommender systems are described, including their typical problems. Options of the hybridization of these systems as well as examples of the popular music recommender systems are mentioned. There also is an outline of their functioning. The following section is focused on the parameterization of musical pieces and is devoted to the description of particular most used parameters. The next section is devoted to the content-based music recommender system design itself, including the defining of particular parameters that are used to differentiate musical recordings using the algorithm mRMR and other procedures. The recommender system design as such is oriented to the classification method k-nearest neighbors. The attention is also paid to the model of user taste recorded by Rocchio algorithm. In the next section the system is implemented according to the design. There is also described its functionality including the background processes. The final part of this work is focused on system testing and evaluation.
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Vamp Plugin for Sonic Visualiser
Pilát, Peter ; Zvončák, Vojtěch (referee) ; Kiska, Tomáš (advisor)
In my Bachelor Thesis, I devote myself to obtaining information from music, the way it can be obtained, the aspect of musical information, and the use of the methods themselves. Then I analyze content-oriented music management methods and also include parameterisation of music recordings and audio signal overall. After familiarizing with the specific parameterization tools to implement the Vamp plugin, which are Sonic Visualiser and Sonic Anotator, I characterize the Vamp Plugin and explain in detail its composition. As explained in the manuals and the calculations in progress, the RMS calculation function of the given signal with the possibility of segmentation functions as well as the function of displaying the sound rate or possible changes in the track temperature. Last but not least, we mention the possible use of these supplements in the future and in different sectors.
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Recognizing the historical period of interpretation based on the music signal parameterization
Král, Vítězslav ; Mucha, Ján (referee) ; Kiska, Tomáš (advisor)
The aim of this semestral work is to summarize the existing knowledge from the area of comparison of musical recordings and to implement an evaluation system for determining the period of creation using the music signal parameterization. In the first part of this work are describe representations which can music take. Next, there is a cross-section of parameters that can be extracted from music recordings provides information on the dynamics, tempo, color, or time development of the music’s recording. In the second part is described evaluation system and its individual sub-blocks. The input data for this evaluation system is a database of 56 sound recordings of the first movement of Beethoven’s 5th Symphony. The last chapter is dedicated to a summary of the achieved results.
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Sound records comparison using timbre features
Miklánek, Štěpán ; Schimmel, Jiří (referee) ; Kiska, Tomáš (advisor)
This thesis deals with research of musical features, which are describing music recordings relating to timbre. First chapter deals with historical development and modern approach in a discipline called Music Information Retrieval (MIR), further there is a description of music processing from the perspective of music theory and digital signal processing. Then followed by a description of signal pre-processing. This part is very important when retrieving features from music recordings. In chapter concerned about retrieving features there are summarized all common features used when retrieving information from musical recordings with main concern to timbral features. A database of music recordings and a feature retrieving system is introduced. The last chapter deals with individual analysis of timbral features.
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Search in Music Signals
Skála, František ; Szőke, Igor (referee) ; Černocký, Jan (advisor)
This work contains overview of methods used in the area of Music Information Retrieval, mainly for purposes of searching of musical recordings. Several existing services in the areas of music identification and searching are presented and their methods for unique song identification are described. This work also focuses on possible modifications of these algorithms for searching of cover versions of songs and for the possibility of searching based on voice created examples.
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