Given the ever-increasing volume of music created and released every day, it has never been more important to study automatic music tagging. In this paper, we present an ensemble-based convolutional neural network (CNN) model trained using various loss functions for tagging musical genres from audio. We investigate the effect of different loss functions and resampling strategies on prediction performance, finding that using focal loss improves overall performance on the the MTG-Jamendo dataset: an imbalanced, multi-label dataset with over 18,000 songs in the public domain, containing 57 labels. Additionally, we report results from varying the receptive field on our base classifier-a CNN-based architecture trained using Mel spectrograms-which also results in a model performance boost and state-of-the-art performance on the Jamendo dataset. We conclude that the choice of the loss function is paramount for improving on existing methods in music tagging, particularly in the presence of class imbalance.

Whether in a happy mood or a sad mood, humans enjoy listening to music. In this paper, we introduce a novel method to identify auditory features that best predict listener-reported enjoyment ratings by splitting the features into qualitative feature groups, then training predictive models on these feature groups and comparing prediction performance. Using audio features that relate to dynamics, timbre, harmony, and rhythm, we predicted continuous enjoyment ratings for a set of happy and sad songs. We found that a distributed lag model with Ll regularization best predicted these responses and that timbre-related features were most relevant for predicting enjoyment ratings in happy music, while harmony-related features were most relevant to predicting enjoyment ratings in sad music. This work adds to our understanding of how music influences affective human experience.

Music has a powerful influence on a listener's emotions. In this paper, we represent lyrics and chords in a shared vector space using a phrase-aligned chord-and-lyrics corpus. We show that models that use these shared representations predict a listener's emotion while hearing musical passages better than models that do not use these representations. Additionally, we conduct a visual analysis of these learnt shared vector representations and explain how they support existing theories in music. This work adds to our understanding of how lyrics and chords interact with one another in music and bears applications in music emotion recognition tasks and music information retrieval.

Music has a powerful influence on human experience. In this paper, we investigate how music affects brain activity, physiological response, and human-reported behavior. Using auditory features related to dynamics, timbre, harmony, rhythm, and register, we predicted brain activity in the form of phase synchronizations in bilateral Heschl's gyri and superior temporal gyri; physiological response in the form of galvanic skin response and heart activity; and emotional experience in the form of continuous, subjective descriptions reported by music listeners. We found that using multivariate time series models with attention mechanisms are effective in predicting emotional ratings, while vector-autoregressive models are effective in predicting involuntary human responses. Musical features related to dynamics, register, rhythm, and harmony were found to be particularly helpful in predicting these human reactions. This work adds to our understanding of how music affects multimodal human experience and has applications in affective computing, music emotion recognition, neuroscience, and music information retrieval.

Traditionally a dedicated level designer has to create every aspect of a level by hand, distribute and gather people to test it. Especially for multiplayer maps, the cyclic process of developing and testing can be quite cumbersome and time consuming. This paper presents a novel approach to provide live generation of levels for such multiplayer maps through procedural content generation and interactive evolution. The approach is demonstrated in the FPSEvolver video game that aims at replicating the look and feel of popular games like Counter-Strike. Without leaving the game a group of players can generate, play and improve levels to fit their particular preferences by voting on a selection of evolving levels. This approach focuses on maintaining the level design principles for modern first-person shooters that encourages good engagements between players, in the popular game mode “bomb defusal”. The presented approach can generate enjoyable maps that adapt to the preferences of players across a range of skill levels. Several distinct types of levels were created during testing, which range from open to closed, and complex to simple, each fitting closely with what the different players consider a good map.