![]() The new paradigm avoids the inter-trial effect on EEG data. Considering that the EEG-based music emotion classification can be influenced by the inter-trial effect, we design a novel single-trial music listening paradigm. The second work of music emotion analysis is based on the EEG of the audience. Empirical experiments validate that our method has achieved good classification result by learning from the labels provided by humans. Inspired by the concepts of superposition state and collapse in quantum mechanism, we model the emotion measurement process by a new designed convolutional neural network. The first work is a novel multi-label emotion classification model named quantum convolutional neural network (QCNN), which classify the music according to the audio signal. For the music emotion analysis, we deliver two pieces of works. Some interesting neuroscience observations are also reported. Several empirical experiments are performed, and the results validate the effectiveness of the proposed method. Based on this paradigm, we propose a novel computational model to evaluate the musicality for machine-composed music. ![]() A new psychological paradigm employing auditory stimuli with different extent of musicality are designed. To provide more convincing evaluation of the machine-composed music, we propose to analyze the brainwave of audience by using electroencephalography (EEG). A series of experiments validate the effectiveness of the proposed algorithmic composition technique. Specifically, the musicality game makes the output music samples follow the distribution of human-composed music examples, while the novelty game makes the output samples to be far from the nearest human example. Two games named musicality game and novelty game are designed and optimized alternatively. To achieve both good musicality and novelty of machine-composed music, we propose a musicality-novelty generative adversarial neural nets (MNGANs) model. We utilize deep learning, a powerful artificial intelligence technology, in algorithmic composition. Algorithmic composition enables the computer to compose music just like human musician. This thesis investigates new artificial intelligence techniques for music composition and emotion analysis. People compose music for deliver their mind. Music is a universal feature in all human societies. Hong Kong Polytechnic University - DissertationsĪrtificial intelligence - Musical applications Artificial intelligence in music : composition and emotion
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