Deep Learning to Generate Pop Music Instrumentals Based on a Hybrid CNN-Transformer Model

Contemporarily, deep learning approaches have been widely implemented in music generation. This paper presents an innovative approach to multi-track music generation using a hybrid CNN-Transformer architecture. Trained on a dataset of 50 pop music instrumentals, the system processes MIDI files through a sophisticated preprocessing pipeline that categorizes instruments into seven standardized groups. The architecture combines track-specific CNN encoders with a transformer-based sequence model, achieving remarkable improvements in cross-track coherence and rhythmic stability. Training results show consistent improvement over 197 epochs, with the loss decreasing from 5.06 to 0.27. Quantitative evaluation demonstrates superior performance compared to baselines, with note coherence, rhythm accuracy, and cross-track synchronization metrics all showing significant improvements. The system successfully generates coherent multi-instrumental compositions while maintaining individual track characteristics, providing a robust framework for AI-assisted music composition. The model architecture attempts to addresses key challenges in multi-track music generation through its specialized preprocessing pipeline, hybrid neural network design, and multi-track generation approach. This implementation establishes a foundation for future developments in computer-assisted composition, with potential applications in automated music production and interactive composition systems.