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With the rapid development of music digitization and online streaming services, automatic analysis and classification of music content has become an urgent need. This research focuses on music sentiment analysis, which is the identification and classification of emotions expressed by music through algorithms. The study defines and classifies possible emotions in music. Then, advanced artificial intelligence techniques, including traditional machine learning and deep learning methods, were employed to perform sentiment analysis on music fragments. In the process of creating and validating the model, the combination of convolutional neural network and long term memory network shows excellent performance in various performance indicators. However, for some complex or culturally ambiguous music fragments, the model may also suffer from misclassification problems. This provides the direction for further optimization of future research aimed at achieving more accurate music emotion analysis. [ABSTRACT FROM AUTHOR]

The emotion recognition of medium audio/video in affective computing has important application value for deep cognition in human-computer interaction (HCI)/brain-computer interaction (BCI) and other fields. Especially in the modern distance education, music emotion analysis can be used as one of the important techniques for real-time evaluation of teaching process. In complex dance scenes, the accuracy of music emotion analysis with traditional methods is not high. Therefore, this paper proposes a novel long short-term memory (LSTM) network model for multimodal music emotion analysis in affective computing. Dual-channel LSTM is used to simulate human auditory and visual processing pathways respectively to process the emotional information of music and facial expressions. Then, we train and test the model on an open bi-modal music dataset. Based on the LSTM model, the analytic hierarchy process (AHP) is introduced to fuse weighted feature at decision level. Finally, experiments show that the proposed method can effectively improve the recognition rate, and save a lot of training time.

After 20 years of extensive study in psychology, some musical factors have been identified that can evoke certain kinds of emotions. However, the underlying mechanism of the relationship between music and emotion remains unanswered. This paper intends to find the genuine correlates of music emotion by exploring a systematic and quantitative framework. The task is formulated as a dimensionality reduction problem, which seeks the complete and compact feature set with intrinsic correlates for the given objectives. Since a song generally elicits more than one emotion, we explore dimensionality reduction techniques for multi-label classification. One challenging problem is that the hard label cannot represent the extent of the emotion and it is also difficult to ask the subjects to quantize their feelings. This work tries utilizing the electroencephalography (EEG) signal to solve this challenge. A learning scheme called EEG-based emotion smoothing (E^2S) and a bilinear multi-emotion similarity preserving embedding (BME-SPE) algorithm are proposed. We validate the effectiveness of the proposed framework on standard dataset CAL-500. Several influential correlates have been identified and the classification via those correlates has achieved good performance. We build a Chinese music dataset according to the identified correlates and find that the music from different cultures may share similar emotions. [ABSTRACT FROM PUBLISHER]

Music can convey and evoke powerful emotions. This amazing ability has not only fascinated the general public but also attracted the researchers from different fields to discover the relationship between music and emotion. Psychologists have indicated that some specific characters of rhythm, harmony, and melody can evoke certain kinds of emotions. Those hypotheses are based on real life experience and proved by psychological paradigms on human beings. Aiming at the same target, this paper intends to design a systematic and quantitative framework, and answer three widely interested questions: 1) what are the intrinsic features embedded in music signal that essentially evoke human emotions; 2) to what extent these features influence human emotions; and 3) whether the findings from computational models are consistent with the existing research results from psychology. We formulate these tasks as a multi-label dimensionality reduction problem and propose an algorithm called multi-emotion similarity preserving embedding (ME-SPE). To adapt to the second-order music signals, we extend ME-SPE to its bilinear version. The proposed techniques show good performance in two standard music emotion datasets. Moreover, they demonstrate some interesting results for further research in this interdisciplinary topic. [ABSTRACT FROM PUBLISHER]