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Nova podatkovna zbirka in evalvacija algoritmov za ocenjevanje razpolozenja v glasbi
Godec, Primož (Author), Marolt, Matija (Mentor) More about this mentor... This link opens in a new window

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Abstract
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.

Language:Slovenian
Keywords:glasba, razpolozenje, custva, algoritem za ocenjevanje razpolozenja
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2014
Views:634
Downloads:175
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Secondary language

Language:English
Title:A new dataset and algorithm evaluation for mood estimation in music
Abstract:
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.

Keywords:music, mood, emotions, mood clasication algorithms

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