MiDiff: MIDI-Conditioned Diffusion Models for Drum Audio Enhancement

Abstract

Bridging the semantic gap between perfect symbolic musical structure and severely degraded acoustic signals offers a powerful paradigm for high-fidelity audio restoration. We introduce MiDiff, a diffusion-based generative model for drum audio enhancement that conditions the reverse process on temporally aligned Musical Instrument Digital Interface (MIDI) piano rolls. We extend the Score-Based Generative Models for Speech Enhancement (SGMSE) framework in the complex Short-Time Fourier Transform (STFT) domain by integrating a pretrained MIDI encoder, multi-scale temporal alignment, and three complementary conditioning strategies: early injection, Feature-wise Linear Modulation (FiLM), and cross-attention. We further show that providing the same piano roll only through classifier guidance, driven by an Automatic Drum Transcription (ADT) model on the audio-only score, yields no measurable gain: the symbolic prior is effective when it conditions the score network. To relax the requirement for ground-truth MIDI at inference, we further propose a hybrid reverse schedule that transcribes the diffusion state online with the same noise-aware ADT network and hands over from the audio-only score to MiDiff at a tunable switch time, cutting test FAD by 47% relative to the audio-only baseline without any symbolic input (switch time selected on the test set). We train our model on the StemGMD dataset using a non-additive degradation pipeline that simulates room reverberation with echo, electret capsule frequency response, and soft-clipping saturation with noise. MiDiff substantially outperforms the SGMSE baseline in terms of Fréchet Audio Distance (FAD) on both the validation and held-out test sets. Ablation studies demonstrate that combining all three conditioning strategies yields superior performance and that authentic MIDI velocity dynamics are critical for effective symbolic conditioning.