Music is categorized into five emotional classes, and continuous emotion perception is employed, representing music emotion as a point on the two-dimensional plane defined by two sentiment variables. An artificial labeling method establishes the positional ranges of the five emotion categories on the emotional plane. Regression methods are then used to determine the relationship between valence-arousal (VA) values and music features, transforming the music emotion classification problem into a regression problem. A regression-based music emotion classification system is designed and implemented, comprising training and testing components. The training phase employs polynomial regression, support vector regression, and k-plane piecewise regression to derive the regression model. In the testing phase, the input music data are processed through regression to predict VA values for classification, with system performance evaluated by classification accuracy. The results indicate that combining support vector regression and k-plane piecewise regression improves accuracy by three to four percentage points compared to using either algorithm alone and by six percentage points compared to traditional support vector machine-based classification. A comparative analysis of various classification algorithms, including support vector machine, K-nearest neighbor, fuzzy neural network, fuzzy K-nearest neighbor, Bayesian classification, and Fisher linear discriminant, revealed that support vector machine, fuzzy K-nearest neighbor, and Fisher linear discriminant achieve accuracy rates exceeding 80%. Finally, a novel \"mixed classifier\" algorithm is proposed, earning a music emotion recognition rate of 84.9%. In [12], the paper addresses the challenging task of music emotion recognition, a key area within music information retrieval. To improve the accuracy of music emotion classification, a novel approach employing an improved Inception-GRU residual network is proposed. This architecture leverages the Inception structure to extract multi-dimensional features through varying receptive fields, incorporating compression, expansion, and re-compression operations for enhanced feature mining. Furthermore, timing signals within the residual network are connected to a gated recurrent unit (GRU) module to capture temporal dependencies. The proposed one-dimensional (1D) CNN with the improved Inception module and GRU was evaluated on the Soundtrack dataset. Fast Fourier transform (FFT) was applied to preprocess the samples and extract spectral characteristics. The experimental results demonstrate that the proposed 1D CNN with the Inception-GRU residual structure outperforms shallow learning methods, support vector machine, random forest, and a deep-learning method based on the visual geometry group (VGG) CNN. Specifically, the proposed method achieved a superior accuracy of 84% in music emotion recognition and classification. Facial expressions are a primary means of human emotional expression. Given the established link between music and mood alteration, the project aims to develop a music player that selects songs based on detected facial expressions, thereby potentially calming the user and providing a more pleasing listening experience [13]. The system captures user images via a webcam interface and employs image segmentation and processing techniques to extract the facial features relevant to emotion recognition. The system selects and plays music appropriate to that mood by identifying the user's expressed emotion. Facial expression recognition has long been recognized as a fundamental method of human communication, providing insights into emotions, feelings, and thoughts. Furthermore, targeted mood alteration through music can be beneficial in addressing issues such as depression and sadness. This expression analysis approach can mitigate health risks and contribute to improved user well-being. In [14], the research explores the impact of music on mood and leverages emotion recognition to understand and influence user feelings.