A new dataset and algorithm evaluation for mood estimation in music

This thesis presents a new dataset of perceived and induced emotions for 200 audio clips. The gathered dataset provides users' perceived and induced emotions for each clip, the association of color, along with demographic and personal data, such as user's emotion state and emotion ratings, genre preference, music experience, among others. With an online survey we collected more than 7000 responses for a dataset of 200 audio excerpts, thus providing about 37 user responses per clip. The focus of the thesis is the evaluation of classifying emotion states in audio with two existing algorithms. Regression algorithm is used to estimate valence and arousal ratings for audio. The Gaiatransform algorithm is used to classify audi clips in five mood clusters. Gaiatransform algorithm also provide probability of presence for six moods in song. Finally, the regression algorithm was used to analyze possible correlation between colors and mood in valence-arousal space

A new dataset and algorithm evaluation for mood estimation in music

V diplomskem delu je predstavljena nova podatkovna zbirka, ki vsebuje podatke o razpoloženju za 200 glasbenih odlomkov. Podatkovna zbirka vključuje podatke o razpoloženju prisotnem v glasbi in o razpoloženju, ki ga glasba vzbudi pri udeležencu. Vključuje tudi podatke o razpoloženju opisanem z barvo, nekatere demografske podatke, udeleženčevo trenutno razpoloženje, podatke o udeleženčevi predstavi razpoloženja glede na prijetnost in aktivnost, najljubše žanre in druge. S spletno anketo smo v povprečju zbrali 37 odzivov na glasbeni odlomek. Predstavljena je evalvacijo dveh algoritmov za ocenjevanje razpoloženja iz glasbe. Regresijski algoritem smo uporabili za ocenjevanje prijetnosti in aktivnosti v glasbi. Drugi je algoritem Gaiatransform, ki glasbo klasificira v pet gruč glede na razpoloženje. Za zaključek smo analizirali korelacijo med razpoloženjem in barvami v glasbenem odlomku, kar smo naredili z napovedovanjem razpoloženja iz podatka o barvi z uporabo regresijskega algoritma.This thesis presents a new dataset of perceived and induced emotions for 200 audio clips. The gathered dataset provides users\u27 perceived and induced emotions for each clip, the association of color, along with demographic and personal data, such as user\u27s emotion state and emotion ratings, genre preference, music experience, among others. With an online survey we collected more than 7000 responses for a dataset of 200 audio excerpts, thus providing about 37 user responses per clip. The focus of the thesis is the evaluation of classifying emotion states in audio with two existing algorithms. Regression algorithm is used to estimate valence and arousal ratings for audio. The Gaiatransform algorithm is used to classify audi clips in five mood clusters. Gaiatransform algorithm also provide probability of presence for six moods in song. Finally, the regression algorithm was used to analyze possible correlation between colors and mood in valence-arousal space

Deep models for image coloring

Barvna fotografija je prišla v vsakdanjo uporabo šele v zadnjih 50 letih, zato so razni arhivi polni črno-belih fotografij, katere bi njihovi lastniki radi obarvali. V ta namen so bili razviti različni algoritmični pristopi. V disertaciji predstavljamo nekaj novih avtomatskih pristopov za barvanje črno-belih slik in videov, ki so osnovani na strojnem učenju in konvolucijskih nevronskih mrežah. Pristope primerjamo s pristopi iz sorodnih del in jih preizkusimo na starih črno-belih slikah. Iz rezultatov je razvidno, da naši pristopi dosegajo kvaliteto barvanja pristopov iz sorodnih del. Naš nov pristop, ki obarva slike po delih, pa izboljša barvanje slik velikosti, ki so različne od tistih, na katerih je bila mreža naučena. Ta pristop je tudi naučen hitreje kot obstoječi pristopi, ki za barvanje uporabljajo celotne slike.Since the color photography came into everyday use in the last fifty years our grandparents are still owning many black and white photographs which we would like to colorize. Researchers are therefore encouraged to develop algorithmic approaches for black and white photographs and video colorization. We have developed a set of automatic approaches based on the machine learning and neural networks, which are using regression and classification. We compared them with approaches from related work. Our approaches reach the quality of colorization comparable to those from related works. Our new approach on image parts improves colorization of images which size is different from those from the training set. This approach is also faster in training than existing approaches that uses full images for learing

A new dataset and algorithm evaluation for mood estimation in music

V diplomskem delu je predstavljena nova podatkovna zbirka, ki vsebuje podatke o razpoloženju za 200 glasbenih odlomkov. Podatkovna zbirka vključuje podatke o razpoloženju prisotnem v glasbi in o razpoloženju, ki ga glasba vzbudi pri udeležencu. Vključuje tudi podatke o razpoloženju opisanem z barvo, nekatere demografske podatke, udeleženčevo trenutno razpoloženje, podatke o udeleženčevi predstavi razpoloženja glede na prijetnost in aktivnost, najljubše žanre in druge. S spletno anketo smo v povprečju zbrali 37 odzivov na glasbeni odlomek. Predstavljena je evalvacijo dveh algoritmov za ocenjevanje razpoloženja iz glasbe. Regresijski algoritem smo uporabili za ocenjevanje prijetnosti in aktivnosti v glasbi. Drugi je algoritem Gaiatransform, ki glasbo klasificira v pet gruč glede na razpoloženje. Za zaključek smo analizirali korelacijo med razpoloženjem in barvami v glasbenem odlomku, kar smo naredili z napovedovanjem razpoloženja iz podatka o barvi z uporabo regresijskega algoritma.This thesis presents a new dataset of perceived and induced emotions for 200 audio clips. The gathered dataset provides users\u27 perceived and induced emotions for each clip, the association of color, along with demographic and personal data, such as user\u27s emotion state and emotion ratings, genre preference, music experience, among others. With an online survey we collected more than 7000 responses for a dataset of 200 audio excerpts, thus providing about 37 user responses per clip. The focus of the thesis is the evaluation of classifying emotion states in audio with two existing algorithms. Regression algorithm is used to estimate valence and arousal ratings for audio. The Gaiatransform algorithm is used to classify audi clips in five mood clusters. Gaiatransform algorithm also provide probability of presence for six moods in song. Finally, the regression algorithm was used to analyze possible correlation between colors and mood in valence-arousal space

Quasars/quasar: 1.9.0

<p>Quasar 1.9.0</p> <p>Based on Orange 3.36.1 and orange-spectroscopy 0.6.11</p> <p>Important changes since orange-spectroscopy 0.6.8:</p> <p>[ENH] Spectra: individual display in its own thread [ENH] Spectra: dask table support [ENH] Spectra: waterfall plot [ENH] Visualization parameters dialog for Spectra and HyperSpectra [ENH] Improved the context menu display on Mac [ENH] A utility function that can easily replace wavenumbers (x) [ENH] Improve palette support (for dark/light mode)[FIX] Fix opening GSF files in Multifile [FIX] owinterpolate: Call commit.deferred in line edit callbacks [FIX] Multifile: fix a bug when wavenumbers differ for less than 1e-6 [FIX] io.opus: Convert visible image loading Exceptions to warnings [FIX] HyperSpectra: handle degenerate (all nan) coordinates and data</p&gt

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae