InstrumentGen: Generating Sample-Based Musical Instruments From Text

We introduce the text-to-instrument task, which aims at generating sample-based musical instruments based on textual prompts. Accordingly, we propose InstrumentGen, a model that extends a text-prompted generative audio framework to condition on instrument family, source type, pitch (across an 88-key spectrum), velocity, and a joint text/audio embedding. Furthermore, we present a differentiable loss function to evaluate the intra-instrument timbral consistency of sample-based instruments. Our results establish a foundational text-to-instrument baseline, extending research in the domain of automatic sample-based instrument generation.

Removing Distortion Effects in Music Using Deep Neural Networks

Audio effects are an essential element in the context of music production, and therefore, modeling analog audio effects has been extensively researched for decades using system-identification methods, circuit simulation, and recently, deep learning. However, only few works tackled the reconstruction of signals that were processed using an audio effect unit. Given the recent advances in music source separation and automatic mixing, the removal of audio effects could facilitate an automatic remixing system. This paper focuses on removing distortion and clipping applied to guitar tracks for music production while presenting a comparative investigation of different deep neural network (DNN) architectures on this task. We achieve exceptionally good results in distortion removal using DNNs for effects that superimpose the clean signal to the distorted signal, while the task is more challenging if the clean signal is not superimposed. Nevertheless, in the latter case, the neural models under evaluation surpass one state-of-the-art declipping system in terms of source-to-distortion ratio, leading to better quality and faster inference.