Download Fast Parametric Matrices for Lossless Feedback Delay Networks This paper presents a framework for designing creative reverbs using parametric orthogonal feedback matrices on Feedback Delay Networks (FDNs) through recursive Kronecker products of 2D rotation and reflection matrices. By parameterizing each 2×2 kernel with a single angle, we construct a family of 2M×2M orthogonal matrices that maintain losslessness while enabling continuous control over network topology. We then exploit their recursive definition to compute the feedback operation with an O(N log₂ N) divide-and-conquer algorithm that matches the Fast Walsh-Hadamard Transform time complexity while offering parametric flexibility. Strategic manipulation of individual kernel angles enables creative sound design applications, such as stereo cross-coupling, selective freeze, and time-varying modulation for resonance breaking.
Download Amp-Space: A Large-Scale Dataset for Fine-Grained Timbre Transformation We release Amp-Space, a large-scale dataset of paired audio
samples: a source audio signal, and an output signal, the result of
a timbre transformation. The types of transformations we study
are from blackbox musical tools (amplifiers, stompboxes, studio
effects) traditionally used to shape the sound of guitar, bass, or
synthesizer sounds. For each sample of transformed audio, the
set of parameters used to create it are given. Samples are from
both real and simulated devices, the latter allowing for orders of
magnitude greater data than found in comparable datasets. We
demonstrate potential use cases of this data by (a) pre-training a
conditional WaveNet model on synthetic data and show that it reduces the number of samples necessary to digitally reproduce a
real musical device, and (b) training a variational autoencoder to
shape a continuous space of timbre transformations for creating
new sounds through interpolation.
Download The Shape of RemiXXXes to Come: Audio Texture Synthesis with Time-frequency Scattering This article explains how to apply time–frequency scattering, a convolutional operator extracting modulations in the time–frequency domain at different rates and scales, to the re-synthesis and manipulation of audio textures. After implementing phase retrieval in the scattering network by gradient backpropagation, we introduce scale-rate DAFx, a class of audio transformations expressed in the domain of time–frequency scattering coefficients. One example of scale-rate DAFx is chirp rate inversion, which causes each sonic event to be locally reversed in time while leaving the arrow of time globally unchanged. Over the past two years, our work has led to the creation of four electroacoustic pieces: FAVN; Modulator (Scattering Transform); Experimental Palimpsest; Inspection (Maida Vale Project) and Inspection II; as well as XAllegroX (Hecker Scattering.m Sequence), a remix of Lorenzo Senni’s XAllegroX, released by Warp Records on a vinyl entitled The Shape of RemiXXXes to Come.
Download Reservoir Computing: a powerful Framework for Nonlinear Audio Processing This paper proposes reservoir computing as a general framework for nonlinear audio processing. Reservoir computing is a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It can in theory approximate arbitrary nonlinear dynamical systems with arbitrary precision, has an inherent temporal processing capability and is therefore well suited for many nonlinear audio processing problems. Always when nonlinear relationships are present in the data and time information is crucial, reservoir computing can be applied. Examples from three application areas are presented: nonlinear system identification of a tube amplifier emulator algorithm, nonlinear audio prediction, as necessary in a wireless transmission of audio where dropouts may occur, and automatic melody transcription out of a polyphonic audio stream, as one example from the big field of music information retrieval. Reservoir computing was able to outperform state-of-the-art alternative models in all studied tasks.
Download The development of an online course in DSP eartraining The authors present a collaborative effort on establishing an online course in DSP eartraining. The paper reports from a preliminary workshop that covered a large range of topics such as eartraining in music education, terminology for sound characterization, e-learning, automated tutoring, DSP techniques, music examples and audio programming. An initial design of the web application is presented as a rich content database with flexible views to allow customized online presentations. Technical risks have already been mitigated through prototyping.
Download A Model of Partial Tracks for Tension-Modulated Steel-String Guitar Tones This paper introduces a spectral model for plucked, steel string tones, based on functional models for time-varying fundamental frequency and inharmonicity coefficient. Techniques to evaluate those analytical values at different time indexes are reviewed and commented. A method to evaluate the unknowns of the fundamental frequency and inharmonicity coefficient functions and match the data of a given tone is presented. Frequency tracks can thereafter be deployed and traced for all values of time. Their accuracy is discussed, and applications for the model are suggested.
Download An Algorithm for a Valved Brass Instrument Synthesis Environment using Finite-Difference Time-Domain Methods with Performance Optimisation This paper presents a physical modelling sound synthesis environment for the production of valved brass instrument sounds. The governing equations of the system are solved using finite-difference time-domain (FDTD) methods and the environment is implemented in the C programming language. Users of the environment can create their own custom instruments and are able to control player parameters such as lip frequency, mouth pressure and valve openings through the use of instrument and score files. The algorithm for sound synthesis is presented in detail along with a discussion of optimisation methods used to reduce run time. Binaries for the environment are available for download online for multiple platforms.
Download NBU: Neural Binaural Upmixing of Stereo Content While immersive music productions have become popular in recent years, music content produced during the last decades has been predominantly mixed for stereo. This paper presents a datadriven approach to automatic binaural upmixing of stereo music. The network architecture HDemucs, previously utilized for both source separation and binauralization, is leveraged for an endto-end approach to binaural upmixing. We employ two distinct datasets, demonstrating that while custom-designed training data enhances the accuracy of spatial positioning, the use of professionally mixed music yields superior spatialization. The trained networks show a capacity to process multiple simultaneous sources individually and add valid binaural cues, effectively positioning sources with an average azimuthal error of less than 11.3 ◦ . A listening test with binaural experts shows it outperforms digital signal processing-based approaches to binauralization of stereo content in terms of spaciousness while preserving audio quality.