arXiv subjects

Jeremy Hyrkas

Publications and source records attributed to Jeremy Hyrkas.

4 recordsLinked to original sources

arXiv2026

Vibrato Matching for Modulation Control and Blending in Sound Mixtures

In sound mixtures of more than one musical source, different vibrato patterns act as a cue that multiple sources are present for both human listeners and source separation algorithms. Matching the vibrato patterns of the signals in the mixture reduces the perception of multiple sources, particularly when the sources play in unison. This work introduces the vibrato matching algorithm, which first suppresses vibrato in a target signal and then transfers vibrato from a source signal to the target. An existing vibrato suppression algorithm is combined with a new algorithm for vibrato transfer, which imparts frequency modulation and amplitude modulation to the harmonics of the target signal, and amplitude modulation onto the spectral envelope of the non-harmonic residual component. Examples demonstrate the algorithm's utility as a vibrato control mechanism and as a tool for blending sound sources. Matching vibrato degrades the performance of source separation algorithms, suggesting a similar degradation in listeners ability to detect the presence of multiple sources.

cs.SD↗

arXiv2025

Real-time implementation of vibrato transfer as an audio effect

An algorithm for deriving delay functions based on real examples of vibrato was recently introduced and can be used to perform a vibrato transfer, in which the vibrato pattern of a target signal is imparted onto an incoming sound using a delay line. The algorithm contains methods that computationally restrict a real-time implementation. Here, a real-time approximation is presented that incorporates an efficient fundamental frequency estimation algorithm and time-domain polyphase IIR filters that approximate an analytic signal. The vibrato transfer algorithm is further supplemented with a proposed method to transfer the amplitude modulation of the target sound, moving this method beyond the capabilities of typical delay-based vibrato effects. Modifications to the original algorithm for real-time use are detailed here and available as source code for an implementation as a VST plugin. This algorithm has applications as an audio effect in sound design, sound morphing, and real-time vibrato control of synthesized sounds.

cs.SD↗

arXiv2025

Preserving Russek's "Summermood" Using Reality Check and a DeltaLab DL-4 Approximation

As a contribution towards ongoing efforts to maintain electroacoustic compositions for live performance, we present a collection of Pure Data patches to preserve and perform Antonio Russek's piece "Summermood" for bass flute and live electronics. The piece, originally written for the DeltaLab DL-4 delay rack unit, contains score markings specific to the DL-4. Here, we approximate the sound and unique functionality of the DL-4 in Pure Data, then refine our implementation to better match the unit on which the piece was performed by comparing settings from the score to two official recordings of the piece. The DL-4 emulation is integrated into a patch for live performance based on the Null Piece, and regression tested using the Reality Check framework for Pure Data. Using this library of patches, Summermood can be brought back into live rotation without the use of the now discontinued DL-4. The patches will be continuously tested to ensure that the piece is playable across computer environments and as the Pure Data programming language is updated.

cs.SD↗

arXiv2021

Network Modulation Synthesis: New Algorithms for Generating Musical Audio Using Autoencoder Networks

A new framework is presented for generating musical audio using autoencoder neural networks. With the presented framework, called network modulation synthesis, users can create synthesis architectures and use novel generative algorithms to more easily move through the complex latent parameter space of an autoencoder model to create audio. Implementations of the new algorithms are provided for the open-source CANNe synthesizer network, and can be applied to other autoencoder networks for audio synthesis. Spectrograms and time-series encoding analysis demonstrate that the new algorithms provide simple mechanisms for users to generate time-varying parameter combinations, and therefore auditory possibilities, that are difficult to create by generating audio from handcrafted encodings.

cs.SD↗