text: 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.
  @cite_30: 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.
  @cite_31: Abstract Mass consumption of large collections of digital music asks for efficient and intuitive ways of organization. In this article, a system is presented which recognizes the evoked music mood on the basis of a wide variety of features, closely sticking to real world conditions. A two-dimensional mood model is discussed in which moods resemble binary values for arousal and valence and an easy and thus user-friendly method is presented through which a fuzzy seven-class mood cluster is deducted. The songs of the ‘Twenty Years of MTV Europe Most Wanted’ music database consisting of recorded pop music tracks serve for evaluation of three groups of features: firstly, traditional features such as rhythm and tonal features, zero crossing rate, cepstral, and MPEG-7 Low Level Descriptors for audio content are extracted. Secondly, lyrics, chord sequences, and genre data are obtained from on-line sources. Thirdly, from all these, the high-level features, musical mode, and as a novel feature, the suited ballroom ...
  @cite_64: 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.
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  @cite_73: The goal of music mood regression is to represent the emotional expression of music pieces as numerical values in a low-dimensional mood space and automatically predict those values for unseen music pieces. Existing studies on this topic usually train and test regression models using music datasets sampled from the same culture source, annotated by people with the same cultural background, or otherwise constructed by the same method. In this study, we explore whether and to what extent regression models trained with samples in one dataset can be applied to predicting valence and arousal values of samples in another dataset. Specifically, three datasets that differ in factors such as cultural backgrounds of stimuli (music) and subjects (annotators), stimulus types and annotation methods are evaluated and the results suggested that cross-cultural and cross-dataset predictions of both valence and arousal values could achieve comparable performance to within-dataset predictions. We also discuss how the generalizability of regression models can be affected by dataset characteristics. Findings of this study may provide valuable insights into music mood regression for nonWestern and other music where training data are scarce.
  @cite_78: Digitization of how people acquire music calls for better music information retrieval techniques, and dimensional emotion tracking is increasingly seen as an attractive approach. Unfortunately, the majority of models we still use are borrowed from other problems that do not suit emotion prediction well, as most of them tend to ignore the temporal dynamics present in music and or the continuous nature of Arousal-Valence space. In this paper we propose the use of Continuous Conditional Random Fields for dimensional emotion tracking and a novel feature vector representation technique. Both approaches result in a substantial improvement on both rootmean-squared error and correlation, for both short and long term measurements. In addition, they can both be easily extended to multimodal approaches to music emotion recognition.
  @cite_60: 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.
  @cite_46: Music emotion recognition is concerned with developing predictive models that comprehend the affective content of musical signals. Recently, a growing number of attempts has been made to model the music emotion as a probability distribution in the valence-arousal (VA) space to better account for the subjectivity. In this paper, we present a novel histogram density modeling approach that models the emotion distribution by a 2-D histogram over the quantized VA space and learns a set of latent histograms to predict the emotion probability density of a song from audio. The proposed model is free from parametric distribution assumptions over the VA space, easy to implement, and extremely fast to train. We also extend our model to deal with the temporal dynamics of time-varying emotion labels. Comprehensive performance study on two larger-scale datasets demonstrates that our approach achieves comparable performance to the state-of-the-art ones, but with much better training and testing efficiency.
  @cite_54: Determining the emotion of a song that best characterizes the affective content of the song is a challenging issue due to the difficulty of collecting reliable ground truth data and the semantic gap between human's perception and the music signal of the song. To address this issue, we represent an emotion as a point in the Cartesian space with valence and arousal as the dimensions and determine the coordinates of a song by the relative emotion of the song with respect to other songs. We also develop an RBF-ListNet algorithm to optimize the ranking-based objective function of our approach. The cognitive load of annotation, the accuracy of emotion recognition, and the subjective quality of the proposed approach are extensively evaluated. Experimental results show that this ranking-based approach simplifies emotion annotation and enhances the reliability of the ground truth. The performance of our algorithm for valence recognition reaches 0.326 in Gamma statistic.
  @cite_77: This study describes the utilization of acoustic cues in communication of emotions in music performance. Three professional guitarists were asked to perform 3 short melodies to communicate anger, sadness, happiness, and fear to listeners. The resulting performances were analyzed with respect to 5 acoustic cues and judged by 30 listeners on adjective scales. Multiple regression analysis was applied to the relationships between (a) the performer's intention and the cues and (b) the listeners' judgments and the cues. The analyses of performers and listeners were related using C. J. Hm' h, K. R. Hammond, and J. L. Hursch's (1964) lens model equation. The results indicated that (a) performers were successful at communicating emotions to listeners, (b) performers' cue utilization was well matched to listeners' cue utilization, and (c) cue utilization was more consistent across different melodies than across different performers. Because of the redundancy of the cues, 2 performers could communicate equally well despite differences in cue utilization.
  @cite_19: 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.
  @cite_43: 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.
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  @cite_68: We adopt a two-layer regression model for music pleasure regression. Pleasure orientation of a song is estimated first, and then different regressors are used to predict degree of pleasure according to the estimated orientation. By using corresponding regressors for each instance, there is a big improvement when we assume the first layer is perfect in comparison with one-layer model. By tuning the confidence threshold of the orientation classifier of the two-layer model, we get improvement over one-layer model.
  @cite_11: Music is composed to be emotionally expressive, and emotional associations provide an especially natural domain for indexing and recommendation in today's vast digital music libraries. But such libraries require powerful automated tools, and the development of systems for automatic prediction of musical emotion presents a myriad challenges. The perceptual nature of musical emotion necessitates the collection of data from human subjects. The interpretation of emotion varies between listeners thus each clip needs to be annotated by a distribution of subjects. In addition, the sharing of large music content libraries for the development of such systems, even for academic research, presents complicated legal issues which vary by country. This work presents a new publicly available dataset for music emotion recognition research and a baseline system. In addressing the difficulties of emotion annotation we have turned to crowdsourcing, using Amazon Mechanical Turk, and have developed a two-stage procedure for filtering out poor quality workers. The dataset consists entirely of creative commons music from the Free Music Archive, which as the name suggests, can be shared freely without penalty. The final dataset contains 1000 songs, each annotated by a minimum of 10 subjects, which is larger than many currently available music emotion dataset.
summary: 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 .