This paper describes a method for estimating the room impulse response (RIR) for a microphone and a sound source located at arbitrary positions from the 3D mesh data of the room.Simulating realistic RIRs with pure physics-driven methods often fails the balance between physical consistency and computational efficiency, hindering application to real-time speech processing.Alternatively, one can use MESH2IR, a fast black-box estimator that consists of an encoder extracting latent code from mesh data with a graph convolutional network (GCN) and a decoder generating the RIR from the latent code.Combining these two approaches, we propose a fast yet physically coherent estimator with interpretable latent code based on differentiable digital signal processing (DDSP).Specifically, the encoder estimates a virtual shoebox room scene that acoustically approximates the real scene, accelerating physical simulation with the differentiable image-source model in the decoder.Our experiments showed that our method outperformed MESH2IR for real mesh data obtained with the depth scanner of Microsoft HoloLens 2, and can provide correct spatial consistency for binaural RIRs.

Supervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets can be extremely costly to obtain for musical mixtures. This raises a need for unsupervised methods. We propose a novel unsupervised model-based deep learning approach to musical source separation. Each source is modelled with a differentiable parametric source-filter model. A neural network is trained to reconstruct the observed mixture as a sum of the sources by estimating the source models' parameters given their fundamental frequencies. At test time, soft masks are obtained from the synthesized source signals. The experimental evaluation on a vocal ensemble separation task shows that the proposed method outperforms learning-free methods based on nonnegative matrix factorization and a supervised deep learning baseline. Integrating domain knowledge in the form of source models into a data-driven method leads to high data efficiency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio. This work makes powerful deep learning based separation usable in scenarios where training data with ground truth is expensive or nonexistent.