Much of the appeal of music lies in its power to convey emotions moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.
As emotion perception is rarely dependent on a single music factor but a combination of them @cite_23 @cite_77 , algorithms used feature descriptors that characterize the loudness, timbre, pitch, rhythm, melody, harmony or lyrics of music @cite_15 @cite_31 @cite_64 @cite_22 . In particular, while it is usually easier to predict arousal using, for example, loudness and timbre features, the prediction of valence has been found more challenging @cite_31 @cite_54 @cite_68 . Cross cultural aspects of emotion perception have also been studied @cite_73 . To exploit the temporal continuity of emotion variation within a piece of music, techniques such as system identification @cite_18 , conditional random fields @cite_19 @cite_78 , hidden Markov models @cite_43 , deep recurrent neural networks @cite_60 , or dynamic probabilistic model @cite_46 have also been proposed. Various approaches and features for MER have been evaluated and compared using benchmarking datasets comprising over 1,000 Creative Commons licensed music pieces from the Free Music Archive, in the 2013 and 2014 MediaEval Emotion in Music' tasks @cite_11 @cite_30 .
@cite_30 @cite_31 @cite_64 @cite_18 @cite_22 @cite_73 @cite_78 @cite_60 @cite_46 @cite_54 @cite_77 @cite_19 @cite_43 @cite_23 @cite_15 @cite_68 @cite_11
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In this paper we describe our approach for the MediaEval's "Emotion in Music" task. Our method consists of deep Long-Short Term Memory Recurrent Neural Networks (LSTM-RNN) for dynamic Arousal and Valence regression, using acoustic and psychoacoustic features extracted from the songs that have been previously proven as effective for emotion prediction in music. Results on the challenge test demonstrate an excellent performance for Arousal estimation (r = 0.613 ± 0.278), but not for Valence (r = 0.026 ± 0.500). Issues regarding the quality of the test set annotations' reliability and distributions are indicated as plausible justifications for these results. By using a subset of the development set that was left out for performance estimation, we could determine that the performance of our approach may be underestimated for Valence (Arousal: r = 0.596 ± 0.386; Valence: r = 0.458 ± 0.551). Music as a form of art is intentionally composed to be emotionally expressive. The emotional features of music are invaluable for music indexing and recommendation. In this paper we present a cross-comparison of automatic emotional analysis of music. We created a public dataset of Creative Commons licensed songs. Using valence and arousal model, the songs were annotated both in terms of the emotions that were expressed by the whole excerpt and dynamically with 1 Hz temporal resolution. Each song received 10 annotations on Amazon Mechanical Turk and the annotations were averaged to form a ground truth. Four different systems from three teams and the organizers were employed to tackle this problem in an open challenge. We compare their performances and discuss the best practices. While the effect of a larger feature set was not very apparent in the static emotion estimation, the combination of a comprehensive feature set and a recurrent neural network that models temporal dependencies has largely outperformed the other proposed methods for dynamic music emotion estimation. Music emotion recognition (MER) field rapidly expanded in the last decade. Many new methods and new audio features are developed to improve the performance of MER algorithms. However, it is very difficult to compare the performance of the new methods because of the data representation diversity and scarcity of publicly available data. In this paper, we address these problems by creating a data set and a benchmark for MER. The data set that we release, a MediaEval Database for Emotional Analysis in Music (DEAM), is the largest available data set of dynamic annotations (valence and arousal annotations for 1,802 songs and song excerpts licensed under Creative Commons with 2Hz time resolution). Using DEAM, we organized the ‘Emotion in Music’ task at MediaEval Multimedia Evaluation Campaign from 2013 to 2015. The benchmark attracted, in total, 21 active teams to participate in the challenge. We analyze the results of the benchmark: the winning algorithms and feature-sets. We also describe the design of the benchmark, the evaluation procedures and the data cleaning and transformations that we suggest. The results from the benchmark suggest that the recurrent neural network based approaches combined with large feature-sets work best for dynamic MER. Content-based retrieval has emerged in the face of content explosion as a promising approach to information access. In this paper, we focus on the challenging issue of recognizing the emotion content of music signals, or music emotion recognition (MER). Specifically, we formulate MER as a regression problem to predict the arousal and valence values (AV values) of each music sample directly. Associated with the AV values, each music sample becomes a point in the arousal-valence plane, so the users can efficiently retrieve the music sample by specifying a desired point in the emotion plane. Because no categorical taxonomy is used, the regression approach is free of the ambiguity inherent to conventional categorical approaches. To improve the performance, we apply principal component analysis to reduce the correlation between arousal and valence, and RReliefF to select important features. An extensive performance study is conducted to evaluate the accuracy of the regression approach for predicting AV values. The best performance evaluated in terms of the R 2 statistics reaches 58.3 for arousal and 28.1 for valence by employing support vector machine as the regressor. We also apply the regression approach to detect the emotion variation within a music selection and find the prediction accuracy superior to existing works. A group-wise MER scheme is also developed to address the subjectivity issue of emotion perception. This paper proposes a novel machine learning approach for the task of on-line continuous-time music mood regression, i.e., low-latency prediction of the time-varying arousal and valence in musical pieces. On the front-end, a large set of segmental acoustic features is extracted to model short-term variations. Then, multi-variate regression is performed by deep recurrent neural networks to model longer-range context and capture the time-varying emotional profile of musical pieces appropriately. Evaluation is done on the 2013 MediaEval Challenge corpus consisting of 1000 pieces annotated in continous time and continuous arousal and valence by crowd-sourcing. In the result, recurrent neural networks outperform SVR and feedforward neural networks both in continuous-time and static music mood regression, and achieve an R 2 of up to .70 and .50 with arousal and valence annotations. Abstract Music's allure lies in its power to stir the emotions. But the relation between the physical properties of an acoustic signal and its emotional impact remains an open area of research. This paper reports the results and possible implications of a pilot study and survey used to construct an emotion index for subjective ratings of music. The dimensions of pleasure and arousal exhibit high reliability. Eighty-five participants' ratings of 100 song excerpts are used to benchmark the predictive accuracy of several combinations of acoustic preprocessing and statistical learning algorithms. The Euclidean distance between acoustic representations of an excerpt and corresponding emotion-weighted visualizations of a corpus of music excerpts provided predictor variables for linear regression that resulted in the highest predictive accuracy of mean pleasure and arousal values of test songs. This new technique also generated visualizations that show how rhythm, pitch, and loudness interrelate to influence our... We propose an approach to the dimensional music emotion recognition (MER) problem, combining both standard and melodic audio features. The dataset proposed by Yang is used, which consists of 189 audio clips. From the audio data, 458 standard features and 98 melodic features were extracted. We experimented with several supervised learning and feature selection strategies to evaluate the proposed approach. Employing only standard audio features, the best attained performance was 63.2 and 35.2 for arousal and valence prediction, respectively (R statistics). Combining standard audio with melodic features, results improved to 67.4 and 40.6 , for arousal and valence, respectively. To the best of our knowledge, these are the best results attained so far with this dataset. The goal of the “Emotion in Music” task in MediaEval 2015 is to automatically estimate the emotions expressed by music (in terms of Arousal and Valence) in a time-continuous fashion. In this paper, considering the high context correlation among the music feature sequence, we study several multiscale approaches at different levels, including acoustic feature learning with Deep Brief Networks (DBNs) followed a modified Autoencoder (AE), bi-directional Long-Short Term Memory Recurrent Neural Networks (BLSTM-RNNs) based multi-scale regression fusion with Extreme Learning Machine (ELM), and hierarchical prediction with Support Vector Regression (SVR). The evaluation performances of all runs submitted are significantly better than the baseline provided by the organizers, illustrating the effectiveness of the proposed approaches. The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level cognitive or semantic aspects of music such as emotions, genre, and similarity. This paper addresses the specific hypothesis whether temporal information is essential for predicting expressed emotions in music, as a prototypical example of a cognitive aspect of music. We propose to test this hypothesis using a novel processing pipeline: 1) Extracting audio features for each track resulting in a multivariate "feature time series". 2) Using generative models to represent these time series (acquiring a complete track representation). Specifically, we explore the Gaussian Mixture model, Vector Quantization, Autoregressive model, Markov and Hidden Markov models. 3) Utilizing the generative models in a discriminative setting by selecting the Probability Product Kernel as the natural kernel for all considered track representations. We evaluate the representations using a kernel based model specifically extended to support the robust two-alternative forced choice self-report paradigm, used for eliciting expressed emotions in music. The methods are evaluated using two data sets and show increased predictive performance using temporal information, thus supporting the overall hypothesis. Emotions are often perceived by humans through a series of multimodal cues, such as verbal expressions, facial expressions and gestures. In order to recognise emotions automatically, reliable emotional labels are required to learn a mapping from human expressions to corresponding emotions. Dimensional emotion models have become popular and have been widely applied for annotating emotions continuously in the time domain. However, the statistical relationship between emotional dimensions is rarely studied. This paper provides a solution to automatic emotion recognition for the Audio Visual Emotion Challenge (AVEC) 2018. The objective is to find a robust way to detect emotions using more reliable emotion annotations in the valence and arousal dimensions. The two main contributions of this paper are: 1) the proposal of a new approach capable of generating more dependable emotional ratings for both arousal and valence from multiple annotators by extracting consistent annotation features; 2) the exploration of the valence and arousal distribution using outlier detection methods, which shows a specific oblique elliptic shape. With the learned distribution, we are able to detect the prediction outliers based on their local density deviations and correct them towards the learned distribution. The proposed method performance is evaluated on the RECOLA database containing audio, video and physiological recordings. Our results show that a moving average filter is sufficient to remove the incidental errors in annotations. The unsupervised dimensionality reduction approaches could be used to determine a gold standard annotations from multiple annotations. Compared with the baseline model of AVEC 2018, our approach improved the arousal and valence prediction of concordance correlation coefficient significantly to respectively 0.821 and 0.589. The continuous dimensional emotion can depict subtlety and complexity of emotional change, which is an inherently challenging problem with growing attention. This paper presents our automatic prediction of dimensional emotional state for Audio-Visual Emotion Challenge (AVEC 2017), which uses multi-features and fusion across all available modalities. Besides the baseline features provided by the organizers, we also extract other acoustic audio feature sets, appearance features and deep visual features as complementary features. Each type of feature is trained using Long Short-Term Memory Recurrent Neutral Network (LSTM-RNN) for every dimensional emotion prediction separately considering annotation delay and temporal pooling. To overcome overfitting problem, robust models are chosen carefully for individual model. Finally, multimodal emotion fusion is achieved by utilizing Support Vector Regression (SVR) with the estimates from different feature sets in decision level fusion. The experimental results indicate that our extracted features are beneficial to performance improvement and our system design achieves very promising results with Concordant Correlation Coefficient (CCC), which outperform the baseline system on the testing set for arousal of 0.599 vs 0.375 (baseline) and for valence of 0.721 vs 0.466 and for liking 0.295 vs 0.246. While emotion-based music organization is a natural process for humans, quantifying it empirically proves to be a very difficult task, and as such no dominant feature representation for music emotion recognition has yet emerged. Much of the difficulty in developing emotion-based features is the ambiguity of the ground-truth. Even using the smallest time window, opinions about emotion are bound to vary and reflect some disagreement between listeners. In previous work, we have modeled human response labels to music in the arousal-valence (A-V) emotion space with time-varying stochastic distributions. Current methods for automatic detection of emotion in music seek performance increases by combining several feature domains (e.g. loudness, timbre, harmony, rhythm). Such work has focused largely in dimensionality reduction for minor classification performance gains, but has provided little insight into the relationship between audio and emotional associations. In this work, we seek to employ regression-based deep belief networks to learn features directly from magnitude spectra. Taking into account the dynamic nature of music, we investigate combining multiple timescales of aggregated magnitude spectra as a basis for feature learning. Music emotion recognition (MER) is an important topic in music understanding, recommendation, retrieval and human computer interaction. Great success has been achieved by machine learning methods in estimating human emotional response to music. However, few of them pay much attention in semantic interpret for emotion response. In our work, we first train an interpretable model between acoustic audio and emotion. Filter, wrapper and shrinkage methods are applied to select important features. We then apply statistical models to build and explain the emotion model. Extensive experimental results reveal that the shrinkage methods outperform the wrapper methods and the filter methods in arousal emotion. In addition, we observed that only a small set of the extracted features have the key effects to arousal. While, most of our extracted features have small contribution to valence music perception. Ultimately, we obtain a higher average accuracy rate in arousal, compared to that in valence. The appeal of music lies in its ability to express emotions, and it is natural for us to organize music in terms of emotional associations. But the ambiguities of emotions make the determination of a single, unequivocal response label for the mood of a piece of music unrealistic. We address this lack of specificity by modeling human response labels to music in the arousal-valence (A-V) representation of affect as a stochastic distribution. Based upon our collected data, we present and evaluate methods using multiple sets of acoustic features to estimate these mood distributions parametrically using multivariate regression. Furthermore, since the emotional content of music often varies within a song, we explore the estimation of these A-V distributions in a time-varying context, demonstrating the ability of our system to track changes on a short-time basis. Listeners' expectations for melodies and harmonies in tonal music are perhaps the most studied aspect of music cognition. Long debated has been whether faster response times (RTs) to more strongly primed events (in a music theoretic sense) are driven by sensory or cognitive mechanisms, such as repetition of sensory information or activation of cognitive schemata that reflect learned tonal knowledge, respectively. We analyzed over 300 stimuli from 7 priming experiments comprising a broad range of musical material, using a model that transforms raw audio signals through a series of plausible physiological and psychological representations spanning a sensory- cognitive continuum. We show that RTs are modeled, in part, by information in periodicity pitch distributions, chroma vectors, and activations of tonal space-a representation on a toroidal surface of the major minor key relationships in Western tonal music. We show that in tonal space, melodies are grouped by their tonal rather than timbral properties, whereas the reverse is true for the periodicity pitch representation. While tonal space variables explained more of the variation in RTs than did periodicity pitch variables, suggesting a greater contribution of cognitive influences to tonal expectation, a stepwise selection model contained variables from both representations and successfully explained the pattern of RTs across stimulus categories in 4 of the 7 experiments. The addition of closure-a cognitive representation of a specific syntactic relationship- succeeded in explaining results from all 7 experiments. We conclude that multiple representational stages along a sensory- cognitive continuum combine to shape tonal expectations in music. © 2014 American Psychological Association. Automatic emotion recognition is a challenging task which can make great impact on improving natural human computer interactions. In this paper, we present our effort for the Affect Subtask in the Audio Visual Emotion Challenge (AVEC) 2017, which requires participants to perform continuous emotion prediction on three affective dimensions: Arousal, Valence and Likability based on the audiovisual signals. We highlight three aspects of our solutions: 1) we explore and fuse different hand-crafted and deep learned features from all available modalities including acoustic, visual, and textual modalities, and we further consider the interlocutor influence for the acoustic features; 2) we compare the effectiveness of non-temporal model SVR and temporal model LSTM-RNN and show that the LSTM-RNN can not only alleviate the feature engineering efforts such as construction of contextual features and feature delay, but also improve the recognition performance significantly; 3) we apply multi-task learning strategy for collaborative prediction of multiple emotion dimensions with shared representations according to the fact that different emotion dimensions are correlated with each other. Our solutions achieve the CCC of 0.675, 0.756 and 0.509 on arousal, valence, and likability respectively on the challenge testing set, which outperforms the baseline system with corresponding CCC of 0.375, 0.466, and 0.246 on arousal, valence, and likability. Human emotion responses to music are dynamic processes that evolve naturally over time in synchrony with the music. It is because of this dynamic nature that systems which seek to predict emotion in music must necessarily analyze such processes on short-time intervals, modeling not just the relationships between acoustic data and emotion parameters, but how those relationships evolve over time. In this work we seek to model such relationships using a conditional random field (CRF), a powerful graphical model which is trained to predict the conditional probability p(y|x) for a sequence of labels y given a sequence of features x. Treating our features as deterministic, we retain the rich local subtleties present in the data, which is especially applicable to contentbased audio analysis, given the abundance of data in these problems. We train our graphical model on the emotional responses of individual annotators in an 11◊11 quantized representation of the arousal-valence (A-V) space. Our model is fully connected, and can produce estimates of the conditional probability for each A-V bin, allowing us to easily model complex emotion-space distributions (e.g. multimodal) as an A-V heatmap.