Music Emotion Classification with Neural Network Architecture and Librosa

The classification of musical emotions is essential for organizing, searching, and recommending music on modern platforms. Traditional models often rely on raw audio or textual features, which may not fully capture the rich emotional content embedded in music. To address this, we propose a Convolutional Neural Network (CNN)-based model combined with Librosa for feature extraction to classify musical emotions effectively. In the proposed approach, Librosa is used to extract meaningful audio features from music signals, including Mel-frequency cepstral coefficients (MFCCs), chroma features, spectral contrast, and tonettes representations. These features provide a compact and informative representation of the audio, capturing timbral, harmonic, and rhythmic characteristics relevant to emotion recognition. The CNN model is then applied to learn hierarchical patterns from these extracted features. Convolutional layers automatically capture local correlations in the audio features, while pooling layers reduce dimensionality and highlight dominant emotional patterns. This deep learning framework eliminates the need for handcrafted feature combinations, allowing the model to generalize effectively across diverse music samples. By combining Librosa feature extraction with the pattern learning capability of CNNs, the proposed system is able to capture complex emotional relationships in music. This approach offers a robust and scalable solution for automated music emotion classification, supporting applications such as music recommendation, playlist generation, and music analytics in real-world platforms.