Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions

The Modernization of Oriental Music Therapy: Five-Element Music Therapy Combined with Artificial Intelligence

In recent years, music has been regarded as a promising non-pharmacological intervention for a number of physical and mental conditions. Five-elements music therapy-based on the five-element theory-is a unique non-pharmacological therapy of East Asian traditional medicine. It has the potential to effectively provide individualized music therapy to individuals with illness. However, one limitation of this music therapy is that the classification of the five elements and its application is mainly based on subjective judgment. The development of artificial intelligence (AI) has enabled the acoustic analysis of multi-factor sound sources. This can develop five-element music therapy. Here, we discussed the challenges proposed by the future combination of five-element music therapy and AI. Further, we hypothesized that AI may promote its use in the medical field.

Harmonizing minds and machines: survey on transformative power of machine learning in music

This survey explores the symbiotic relationship between Machine Learning (ML) and music, focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere. Beginning with a historical contextualization of the intertwined trajectories of music and technology, the paper discusses the progressive use of ML in music analysis and creation. Emphasis is placed on present applications and future potential. A detailed examination of music information retrieval, automatic music transcription, music recommendation, and algorithmic composition presents state-of-the-art algorithms and their respective functionalities. The paper underscores recent advancements, including ML-assisted music production and emotion-driven music generation. The survey concludes with a prospective contemplation of future directions of ML within music, highlighting the ongoing growth, novel applications, and anticipation of deeper integration of ML across musical domains. This comprehensive study asserts the profound potential of ML to revolutionize the musical landscape and encourages further exploration and advancement in this emerging interdisciplinary field.

The influence of music teaching appreciation on the mental health of college students based on multimedia data analysis

The mental health problem of college students has gradually become the focus of people's attention. The music appreciation course in university is a very effective approach of psychological counseling, and it is urgent to explore the role of music appreciation in psychological adjustment. Therefore, we propose an emotion classification model based on particle swarm optimization (PSO) to study the effect of inter active music appreciation teaching on the mental health of college students. We first extract musical features as input. Then, the extracted music appreciation features generate subtitles of music information. Finally, we weight the above features, input them into the network, modify the network through particle swarm optimization, and output the emotional class of music. The experimental results show that the music emotion classification model has a high classification accuracy of 82.6%, and can obtain the emotional categories included in interactive music appreciation, which is helpful to guide the mental health of college students in music appreciation teaching.

Solfeggio-frequency music exposure reverses cognitive and endocrine deficits evoked by a 24-h light exposure in adult zebrafish

Music therapy has long been used as a non-pharmacological intervention to improve cognitive function and mood in humans. Mounting rodent evidence also supports beneficial impact of music exposure on animal cognitive performance. The zebrafish (Danio rerio) is an important emerging aquatic animal model in translational biomedical and neuroscience research. Here, we evaluate the effects of intermittent (2-h or 6-h twice daily) and continuous (24-h) solfeggio-frequency music exposure on behavioral, cognitive and endocrine parameters in adult zebrafish whose circadian rhythm was disturbed by a 24-h light exposure. Overall, while a 24-h light exposure evokes overt cognitive deficits and elevates zebrafish whole-body cortisol levels, these effects were reversed by solfeggio-frequency music exposure for 2 or 6h twice daily, and by continuous 24-h exposure). Collectively, these findings suggest a positive modulation of cognitive and endocrine responses in adult zebrafish by environmental enrichment via the long-term exposure to music, and reinforces zebrafish as a robust, sensitive model organism for neurocognitive and neuroendocrine research.