Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition

This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with the state-of-the-art method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the 'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.Comment: Accepted for Sound and Music Computing (SMC 2017

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Automated Recognition of Facial Affect Using Deep Neural Networks

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results. Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units for training generalizable and robust classification models. The problem of automated FER especially with images captured in the wild setting is even more challenging since there are subtle differences between various facial emotions. This dissertation presents the recent efforts I made in 1) creating a large annotated database of facial expressions, 2) developing novel DNN-based methods for automated recognition of facial expressions described by two main models of affect, the categorical model and the dimensional model, and 3) developing a robust face detection and emotion recognition system based on our state-of-the-art DNN and trained on our proposed database of facial expressions. Existing annotated databases of facial expressions in the wild are small and mostly cover discrete emotions (aka the categorical model). There are very limited annotated facial databases for affective computing in the continuous dimensional model (e.g., valence and arousal). To address these needs, we developed the largest database of human affect (called AffectNet). For AffectNet, we collected, annotated, and prepared for public distribution a new database of facial emotions in the wild. AffectNet contains more than 1,000,000 facial images from the Internet by querying three major search engines using 1250 emotion related keywords in six different languages. About half of the retrieved images were manually annotated for the presence of seven discrete facial expressions and the intensity of valence and arousal. AffectNet is by far the largest database of facial expression, valence, and arousal in the wild enabling research in automated facial expression recognition in two different emotion models. This dissertation also presents three major and novel DNN-based methods for automated facial affect estimation. The methods are: 1) 3D Inception-ResNet (3DIR), 2) BReGNet, and 3) BReG-NeXt architectures. These methods modify the residual unit -proposed in the original ResNets- with different operations. Comprehensive experiments are conducted to evaluate the performance of each of the proposed methods as well as their efficiency using Affect and few other facial expression databases. Our final proposed method -BReG-NeXt- achieves state-of-the-art results in predicting both dimensional and categorical models of affect with significantly fewer training parameters and less number of FLOPs. Additionally, a robust face detection network is developed based on the BReG-NeXt architecture which leverages AffectNet’s diverse training data and BReG-NeXt’s efficient feature extraction powers

Music Emotion Recognition based on Feature Combination, Deep Learning and Chord Detection

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.As one of the most classic human inventions, music appeared in many artworks, such as songs, movies and theatres. It can be seen as another language, used to express the authors thoughts and emotion. In many cases, music can express the meaning and emotion emerged which is the authors hope and the audience feeling. However, the emotions which appear during human enjoying the music is complex and difficult to precisely explain. Therefore, Music Emotion Recognition (MER) is an interesting research topic in artificial intelligence field for recognising the emotions from the music. The recognition methods and tools for the music signals are growing fast recently. With recent development of the signal processing, machine learning and algorithm optimization, the recognition accuracy is approaching perfection. In this thesis, the research is focused on three differentsignificantpartsofMER,thatarefeatures, learningmethodsandmusicemotion theory, to explain and illustrate how to effectively build MER systems. Firstly, an automatic MER system for classing 4 emotions was proposed where OpenSMILE is used for feature extraction and IS09 feature was selected. After the combination with STAT statistic features, Random Forest classifier produced the best performance than previous systems. It shows that this approach of feature selection and machine learning can indeed improve the accuracy of MER by at least 3.5% from other combinations under suitable parameter setting and the performance of system was improved by new features combination by IS09 and STAT reaching 83.8% accuracy. Secondly, another MER system for 4 emotions was proposed basedon the dynamic property of music signals where the features are extracted from segments of music signals instead of the whole recording in APM database. Then Long Shot-Term Memory (LSTM) deep learning model was used for classification. The model can use the dynamic continuous information between the different time frame segments for more effective emotion recognition. However, the final performance just achieved 65.7% which was not as good as expected. The reason might be that the database is not suitable to the LSTM as the initial thoughts. The information between the segments might be not good enough to improve the performance of recognition in comparison with the traditional methods. The complex deep learning method do not suitable for every database was proved by the conclusion,which shown that the LSTM dynamic deep learning method did not work well in this continuous database. Finally, it was targeted to recognise the emotion by the identification of chord inside as these chords have particular emotion information inside stated in previous theoretical work. The research starts by building a new chord database that uses the Adobe audition to extract the chord clip from the piano chord teaching audio. Then the FFT features based on the 1000 points sampling pre-process data and STAT features were extracted for the selected samples from the database. After the calculation and comparison using Euclidean distance and correlation, the results shown the STAT features work well in most of chords except the Augmented chord. The new approach of recognise 6 emotions from the music was first time used in this research and approached 75% accuracy of chord identification. In summary, the research proposed new MER methods through the three different approaches. Some of them achieved good recognition performance and some of them will have more broad application prospect