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Music mood recognition by machine continues to attract attention from both academia and industry. This thesis explores the hypothesis that the music emotion problem is circular, and is a primary step in determining the efficacy of circular regression as a machine learning method for automatic music mood recognition. This hypothesis is tested through experiments conducted using instances of the two commonly accepted models of affect used in machine learning (categorical and two-dimensional), as well as on an original circular model proposed by the author. Polygonal approximations of circular regression are proposed as a practical way to investigate whether the circularity of the annotations can be exploited. An original dataset assembled and annotated for the models is also presented. Next, the architecture and implementation choices of all three models are given, with an emphasis on the new polygonal approximations of circular regression. Experiments with different polygons demonstrate consistent and in some cases significant improvements over the categorical model on a dataset containing ambiguous extracts (ones for which the human annotators did not fully agree upon). Through a comprehensive analysis of the results, errors and inconsistencies observed, evidence is provided that mood recognition can be improved if approached as a circular problem. Finally, a proposed multi-tagging strategy based on the circular predictions is put forward as a pragmatic method to automatically annotate music based on the circular model. / Graduate / 0984 / 0800 / 0413 / zazz101@hotmail.com

With podcasts being a fast growing audio-only form of media, an effective way of promoting different podcast shows becomes more and more vital to all the stakeholders concerned, including the podcast creators, the podcast streaming platforms, and the podcast listeners. This thesis investigates the relatively little studied topic of automatic podcast trailer generation, with the purpose of en- hancing the overall visibility and publicity of different podcast contents and gen- erating more user engagement in podcast listening. This thesis takes a hotspot- based approach, by specifically defining the vague concept of “hotspot” and designing different appropriate methods for hotspot detection. Different meth- ods are analyzed and compared, and the best methods are selected. The selected methods are then used to construct an automatic podcast trailer generation sys- tem, which consists of four major components and one schema to coordinate the components. The system can take a random podcast episode audio as input and generate an around 1 minute long trailer for it. This thesis also proposes two human-based podcast trailer evaluation approaches, and the evaluation results show that the proposed system outperforms the baseline with a large margin and achieves promising results in terms of both aesthetics and functionality.

Music is created by composers to arouse different emotions and feelings in the listener, and in the case of soundtracks, to support the storytelling of scenes. The goal of this project is to seek the best method to evaluate the emotional content of soundtracks. This emotional content can be measured quantitatively thanks to Russell’s model of valence, arousal and dominance which converts moods labels into numbers. To conduct the analysis, MFCCs and VGGish features were extracted from the soundtracks and used as inputs to a CNN and an LSTM model, in order to study which one achieved a better prediction. A database of 6757 number of soundtracks with their correspondent VAD values was created to perform the mentioned analysis. As an ultimate purpose, the results of the experiments will contribute to the start-up Vionlabs to understand better the content of the movies and, therefore, make a more accurate recommendation on what users want to consume on Video on Demand platforms according to their emotions or moods. / Musik skapas av kompositörer för att väcka olika känslor och känslor hos lyssnaren, och när det gäller ljudspår, för att stödja berättandet av scener. Målet med detta projekt är att söka den bästa metoden för att utvärdera det emotionella innehållet i ljudspår. Detta känslomässiga innehåll kan mätas kvantitativt tack vare Russells modell av valens, upphetsning och dominans som omvandlar stämningsetiketter till siffror. För att genomföra analysen extraherades MFCC: er och VGGish-funktioner från ljudspåren och användes som ingångar till en CNN- och en LSTM-modell för att studera vilken som uppnådde en bättre förutsägelse. En databas med totalt 6757 ljudspår med deras korrespondent acrshort VAD-värden skapades för att utföra den nämnda analysen. Som ett yttersta syfte kommer resultaten av experimenten att bidra till att starta upp Vionlabs för att bättre förstå innehållet i filmerna och därför ge mer exakta rekommendationer på Video on Demand-plattformar baserat på användarnas känslor eller stämningar.