This paper introduces a novel approach for generating a lower layer in multichannel audio upmixing, addressing a gap in existing methods that primarily focus on mid; top layers. Leveraging Harmonic-Percussive Separation (HPS), the proposed framework dynamically adjusts key parameters (separation factor, harmonic attenuation,; phase shift) to enhance percussive components while diffusing harmonic elements. We compared three neural network architectures for this task: LSTM, TCN,; Transformer. Experimental results show comparable perceptual quality; objective metrics across all models, with the TCN being the most balanced; suitable for deployment on edge devices.
