## 1 Introduction
Arrangements and instrumentation alter audio's spectral distribution and sonic characteristics, providing critical inputs for machine learning models. These inputs enhance the accuracy of music classification,addressing diverse needs such as recommendation systems, music management, and emotion analysis [17]. However, the process of music classification should primarily rely on quantitative descriptors of the purely musical aspects of a piece. [PERSON] et al. [18] utilized a novel genre classification framework for low-level signal-based and high-level harmonic features, covering categories like classical, jazz, and pop genres. Similarly, [PERSON] et al. [19] introduced a symbolic audio-based music genre classification framework that categorizes music by analyzing harmonic structures. Regarding deep learning techniques, standard classification models employ convolutional neural networks (CNNs). These models incorporate preprocessing and annotation techniques for input sources, balancing label quality and data volume. This approach addresses the limitations of most existing Sound Event Recognition (SER) datasets, which are often relatively small or domain-specific [20]. In fully automated audio analysis and music feature extraction, Mel-Frequency Cepstral Coefficients (MFCC) play a pivotal role as a feature extraction method. MFCC transforms audio signals into a spectral cepstral representation, capturing the audio's frequency and energy distribution characteristics. Modern research often leverages deep learning models, such as DNN, CNN, and LSTM, using MFCC as input features to improve music classification performance [21]. Apart from MFCC, Chroma features are essential for describing melodic and harmonic characteristics in music by mapping audio data into 12 semitone-based pitch categories. Chroma features capture the fundamental aspects of melody, harmony, and tonality. [PERSON] et al. [22] provide a novel application of Chroma features introduced for music retrieval based on scores and audio. The approach integrates Connectionist Temporal Classification (CTC) loss for training, reducing reliance on strongly aligned training data. Traditional methods typically require precise alignment between scores and audio. Still, CTC loss only necessitates weak annotations (e.g., start and end times of musical themes), significantly simplifying data preparation and improving efficiency. Among various frequency domain analysis techniques, [PERSON] et al. [23] explored the simultaneous use of MFCC, Chroma, and Spectral Contrast for Sound Event Recognition (SER). By implementing CNNs with convolutional attention blocks, the study achieved an average classification accuracy of 82.5%. Additionally, using five-fold cross-validation, the highest accuracy reached 92.71%, demonstrating enhanced generalization capability in feature extraction. Based on theoretical knowledge, the AI social-emotional music model focused on integrating positive emotional elements into classical music. This study aimed to develop an innovative AI model for improving emotion regulation and music literacy for individuals with special needs, including those with disabilities, such as intellectual and developmental disabilities. Since individuals with disabilities may have a higher likelihood of exhibiting social and emotional challenges, including internalizing and externalizing behavioral problems, we propose that implementing an emotional music model could be an effective mechanism for coping with social and emotional conflicts. Research questions included: (1) feature extraction of audio regions of interest in classical music; (2) development of deep learning models applicable to three different social-emotional contexts; and (3) model validation techniques and comparison with current research.