Measuring national mood with music: using machine learning to construct a measure of national valence from audio data.
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ABSTRACT:
SUBMITTER: Benetos E
PROVIDER: S-EPMC8876081 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method.
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Project description:Music sometimes improves performance in sustained attention tasks. But the type of music employed in previous investigations has varied considerably, which can account for equivocal results. Progress has been hampered by lack of a systematic database of music varying in key characteristics like tempo and valence. The aims of this study were to establish a database of popular music varying along the dimensions of tempo and valence and to examine the impact of music varying along these dimensions on restoring attentional resources following performance of a sustained attention to response task (SART) vigil. Sixty-nine participants rated popular musical selections that varied in valence and tempo to establish a database of four musical types: fast tempo positive valence, fast tempo negative valence, slow tempo positive valence, and slow tempo negative valence. A second group of 89 participants performed two blocks of the SART task interspersed with either no break or a rest break consisting of 1 of the 4 types of music or silence. Presenting positive valence music (particularly of slow tempo) during an intermission between two successive blocks of the SART significantly decreased miss rates relative to negative valence music or silence. Results support an attentional restoration theory of the impact of music on sustained attention, rather than arousal theory and demonstrate a means of restoring sustained attention. Further, the results establish the validity of a music database that will facilitate further investigations of the impact of music on performance.
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Project description:Research has shown that mind-wandering, negative mood, and poor wellbeing are closely related, stressing the importance of exploring contexts or tools that can stimulate positive thoughts and images. While music represents a promising option, work on this topic is still scarce with only a few studies published, mainly featuring laboratory or online music listening tasks. Here, I used the experience sampling method for the first time to capture mind-wandering during personal music listening in everyday life, aiming to test for the capacity of music to facilitate beneficial styles of mind-wandering and to explore its experiential characteristics. Twenty-six participants used a smart-phone application that collected reports of thought, mood, and emotion during music listening or other daily-life activities over 10 days. The application was linked to a music playlist, specifically assembled to induce positive and relaxing emotions. Results showed that mind-wandering evoked during music and non-music contexts had overall similar characteristics, although some minor differences were also observed. Most importantly, music-evoked emotions predicted thought valence, thereby indicating music as an effective tool to regulate thoughts via emotion. These findings have important applications for music listening in daily life as well as for the use of music in health interventions.
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Project description:BackgroundGlutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs).MethodsWe used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD.ResultsWe identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells.ConclusionOur study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.
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Project description:Previous research has indicated that, compared to audio-only presentation, audio-visual congruent presentation can lead to a more intense emotional response. In the present study, we investigated the audio-visual integration effect on emotions elicited by positive or negative music and the role of visual information presentation durations. The participants were presented with audio-only condition, audio-visual congruent condition, and audio-visual incongruent condition and then required to judge the intensity of emotional experience elicited by the music. Their emotional responses to the music were measured using self-ratings and physiological aspects, including heart rate, skin temperature, EMG root mean square and prefrontal EEG. Relative to the audio-only presentation, the audio-visual congruent presentation led to a more intense emotional response. More importantly, the audio-visual integration occurred both in the positive music and in the negative music. Furthermore, the audio-visual integration effect was larger for positive music than for negative music; meanwhile the audio-visual integration effect was strongest with the visual information presented within 80s for negative music, which indicated that this integration effect was more likely to occur in the negative music. These results suggest that when the music was positive, the effect of audio-visual integration was greater. When the music was negative, the modulation effect of the presentation durations of visual information on the music-induced emotion was more significant.
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