The aesthetic emotional expression of piano music art in the background of Internet of things

Artwork, generally refers to the work of plastic art. Artwork is divided into many categories, pottery art, traditional Chinese painting, abstract painting, musical instrument, sculpture, cultural relic sculpture, sandstone, imitation sandstone, ornaments, iron art, copper art, stainless steel sculpture and so on. With the continuous influx of artistic works, there are more and more studies on their emotional expression. How to judge whether musical works can bring joy, anger, sadness and joy to people? Is it joy over anger or anger over joy? Now in the era of the Internet of Things, the Internet of Things uses various information sensors, radio frequency identification technology, GPS, infrared sensors, laser scanners and other equipment and technologies to collect any objects and processes that need to be monitored, connected, and interacted in real time. By collecting various information such as sound, light, heat, electricity, mechanics, chemistry, biology, location and so on, and using various possible networks to connect, it can achieve intelligent perception, identification and management of objects and processes. The Internet of Things is an information carrier based on the Internet, traditional telecommunication networks and so on., so that all normal physical objects that can be individually located which can be connected together. The application field of the Internet of Things involves all aspects. The application in the fields of industry, agriculture, environment, transportation, logistics, security and other infrastructure has effectively promoted the intelligent development of these aspects, which making the limited resources more rational use and distribution, thus improving the efficiency and benefit of the industry. The application in household, medical and health, education, finance and service industry, tourism and other fields closely related to life has been greatly improved in terms of service scope, service method and service quality, which has greatly improved people's quality of life. Based on this, this paper mainly studies the aesthetic emotion expression analysis of piano music art in the context of the Internet of Things. It mainly talks about the classification of music characteristics, emotional theoretical models, and emotional induction methods. Finally, the experimental analysis of piano music and the use of brain wave technology are used to analyze the experimental data. The experimental results show that in the process of feature extraction and optimization, this paper optimizes the traditional feature extraction based on power spectral density through cognitive rules, and achieves the effect of feature dimension reduction on the basis of ensuring the recognition rate. This paper uses the topological properties of EEG to classify emotions. The results show that the emotion recognition rate under the four emotional states can reach 67.3%, which is much higher than the current highest level.

The autistic brain can process local but not global emotion regularities in facial and musical sequences

Whether autism spectrum disorder (ASD) is associated with a global processing deficit remains controversial. Global integration requires extraction of regularity across various timescales, yet little is known about how individuals with ASD process regularity at local (short timescale) versus global (long timescale) levels. To this end, we used event-related potentials to investigate whether individuals with ASD would show different neural responses to local (within trial) versus global (across trials) emotion regularities extracted from sequential facial expressions; and if so, whether this visual abnormality would generalize to the music (auditory) domain. Twenty individuals with ASD and 21 age- and IQ-matched individuals with typical development participated in this study. At an early processing stage, ASD participants exhibited preserved neural responses to violations of local emotion regularity for both faces and music. At a later stage, however, there was an absence of neural responses in ASD to violations of global emotion regularity for both faces and music. These findings suggest that the autistic brain responses to emotion regularity are modulated by the timescale of sequential stimuli, and provide insight into the neural mechanisms underlying emotional processing in ASD.

The Emotional Effect of Background Music on Selective Attention of Adults

Daily activities can often be performed while listening to music, which could influence the ability to select relevant stimuli while ignoring distractors. Previous studies have established that the level of arousal of music (e.g., relaxing/stimulating) has the ability to modulate mood and affect the performance of cognitive tasks. The aim of this research was to explore the effect of relaxing and stimulating background music on selective attention. To this aim, 46 healthy adults performed a Stroop-type task in five different sound environments: relaxing music, stimulating music, relaxing music-matched noise, stimulating music-matched noise, and silence. Results showed that response times for incongruent and congruent trials as well as the Stroop interference effect were similar across conditions. Interestingly, results revealed a decreased error rate for congruent trials in the relaxing music condition as compared to the relaxing music-matched noise condition, and a similar tendency between relaxing music and stimulating music-matched noise. Taken together, the absence of difference between background music and silence conditions suggest that they have similar effects on adult’s selective attention capacities, while noise seems to have a detrimental impact, particularly when the task is easier cognitively. In conclusion, the type of sound stimulation in the environment seems to be a factor that can affect cognitive tasks performance.

Musical Emotion Perception in Bimodal Patients: Relative Weighting of Musical Mode and Tempo Cues

Several cues are used to convey musical emotion, the two primary being musical mode and musical tempo. Specifically, major and minor modes tend to be associated with positive and negative valence, respectively, and songs at fast tempi have been associated with more positive valence compared to songs at slow tempi (Balkwill and Thompson, 1999; Webster and Weir, 2005). In Experiment I, we examined the relative weighting of musical tempo and musical mode among adult cochlear implant (CI) users combining electric and contralateral acoustic stimulation, or "bimodal" hearing. Our primary hypothesis was that bimodal listeners would utilize both tempo and mode cues in their musical emotion judgments in a manner similar to normal-hearing listeners. Our secondary hypothesis was that low-frequency (LF) spectral resolution in the non-implanted ear, as quantified via psychophysical tuning curves (PTCs) at 262 and 440 Hz, would be significantly correlated with degree of bimodal benefit for musical emotion perception. In Experiment II, we investigated across-channel spectral resolution using a spectral modulation detection (SMD) task and neural representation of temporal fine structure via the frequency following response (FFR) for a 170-ms /da/ stimulus. Results indicate that CI-alone performance was driven almost exclusively by tempo cues, whereas bimodal listening demonstrated use of both tempo and mode. Additionally, bimodal benefit for musical emotion perception may be correlated with spectral resolution in the non-implanted ear via SMD, as well as neural representation of F0 amplitude via FFR - though further study with a larger sample size is warranted. Thus, contralateral acoustic hearing can offer significant benefit for musical emotion perception, and the degree of benefit may be dependent upon spectral resolution of the non-implanted ear.

Basic emotions expressed in music: factor analyses on intensity ratings by non-musical professional Chinese university students

BACKGROUND

Previous studies of musical emotion largely depended on the lexical approach which suffered from overlaps between emotions.

METHODS

In the present study, we explored emotional domains through a dimensional approach based on the intensity ratings on the emotion perceived in music. Altogether, 488 university students were invited to listen to 60 musical excerpts (most of them classical), to rate the intensity of emotion perceived without naming the emotion. Later, we conducted the exploratory factor analysis on the intensity ratings to look for the latent structures of musical emotion and then applied the confirmatory factor analysis to verify the validity of the proposed model of emotional structure.

RESULTS

After first- and second-order factor analyses, seven emotional factors (domains, with 38 musical excerpts) were identified: Happiness, Tenderness, Sadness, Passion, Anger, Anxiousness, and Depression, which formed a satisfactory model. No gender difference was found regarding the perceived intensity of musical emotion.

CONCLUSION

Our study has offered evidence to delineate basic musical emotions into seven domains.

[An improved electroencephalogram feature extraction algorithm and its application in emotion recognition]

The result of the emotional state induced by music may provide theoretical support and help for assisted music therapy. The key to assessing the state of emotion is feature extraction of the emotional electroencephalogram (EEG). In this paper, we study the performance optimization of the feature extraction algorithm. A public multimodal database for emotion analysis using physiological signals (DEAP) proposed by Koelstra et al. was applied. Eight kinds of positive and negative emotions were extracted from the dataset, representing the data of fourteen channels from the different regions of brain. Based on wavelet transform, δ, θ, α and β rhythms were extracted. This paper analyzed and compared the performances of three kinds of EEG features for emotion classification, namely wavelet features (wavelet coefficients energy and wavelet entropy), approximate entropy and Hurst exponent. On this basis, an EEG feature fusion algorithm based on principal component analysis (PCA) was proposed. The principal component with a cumulative contribution rate more than 85% was retained, and the parameters which greatly varied in characteristic root were selected. The support vector machine was used to assess the state of emotion. The results showed that the average accuracy rates of emotional classification with wavelet features, approximate entropy and Hurst exponent were respectively 73.15%, 50.00% and 45.54%. By combining these three methods, the features fused with PCA possessed an accuracy of about 85%. The obtained classification accuracy by using the proposed fusion algorithm based on PCA was improved at least 12% than that by using single feature, providing assistance for emotional EEG feature extraction and music therapy.

Temporal dynamics of musical emotions examined through intersubject synchrony of brain activity

To study emotional reactions to music, it is important to consider the temporal dynamics of both affective responses and underlying brain activity. Here, we investigated emotions induced by music using functional magnetic resonance imaging (fMRI) with a data-driven approach based on intersubject correlations (ISC). This method allowed us to identify moments in the music that produced similar brain activity (i.e. synchrony) among listeners under relatively natural listening conditions. Continuous ratings of subjective pleasantness and arousal elicited by the music were also obtained for the music outside of the scanner. Our results reveal synchronous activations in left amygdala, left insula and right caudate nucleus that were associated with higher arousal, whereas positive valence ratings correlated with decreases in amygdala and caudate activity. Additional analyses showed that synchronous amygdala responses were driven by energy-related features in the music such as root mean square and dissonance, while synchrony in insula was additionally sensitive to acoustic event density. Intersubject synchrony also occurred in the left nucleus accumbens, a region critically implicated in reward processing. Our study demonstrates the feasibility and usefulness of an approach based on ISC to explore the temporal dynamics of music perception and emotion in naturalistic conditions.

Preattentive processing of emotional musical tones: a multidimensional scaling and ERP study

Musical emotion can be conveyed by subtle variations in timbre. Here, we investigated whether the brain is capable to discriminate tones differing in emotional expression by recording event-related potentials (ERPs) in an oddball paradigm under preattentive listening conditions. First, using multidimensional Fechnerian scaling, pairs of violin tones played with a happy or sad intonation were rated same or different by a group of non-musicians. Three happy and three sad tones were selected for the ERP experiment. The Fechnerian distances between tones within an emotion were in the same range as the distances between tones of different emotions. In two conditions, either 3 happy and 1 sad or 3 sad and 1 happy tone were presented in pseudo-random order. A mismatch negativity for the emotional deviant was observed, indicating that in spite of considerable perceptual differences between the three equiprobable tones of the standard emotion, a template was formed based on timbral cues against which the emotional deviant was compared. Based on Juslin's assumption of redundant code usage, we propose that tones were grouped together, because they were identified as belonging to one emotional category based on different emotion-specific cues. These results indicate that the brain forms an emotional memory trace at a preattentive level and thus, extends previous investigations in which emotional deviance was confounded with physical dissimilarity. Differences between sad and happy tones were observed which might be due to the fact that the happy emotion is mostly communicated by suprasegmental features.