Download Neural Modeling of Magnetic Tape Recorders The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today’s sound practitioners due to the imperfections embedded in the physics of the magnetic recording process. This paper proposes a method for digitally emulating this character using neural networks. The signal chain of the proposed system consists of three main components: the hysteretic nonlinearity and filtering jointly produced by the magnetic recording process as well as the record and playback amplifiers, the fluctuating delay originating from the tape transport, and the combined additive noise component from various electromagnetic origins. In our approach, the hysteretic nonlinear block is modeled using a recurrent neural network, while the delay trajectories and the noise component are generated using separate diffusion models, which employ U-net deep convolutional neural networks. According to the conducted objective evaluation, the proposed architecture faithfully captures the character of the magnetic tape recorder. The results of this study can be used to construct virtual replicas of vintage sound recording devices with applications in music production and audio antiquing tasks.
Download Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial Approaches Accurately estimating nonlinear audio effects without access to
paired input-output signals remains a challenging problem. This
work studies unsupervised probabilistic approaches for solving this
task. We introduce a method, novel for this application, based
on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using blackand gray-box models. This study compares this method with a
previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the
effect operator and varying lengths of available effected recordings. Through experiments on guitar distortion effects, we show
that the diffusion-based approach provides more stable results and
is less sensitive to data availability, while the adversarial approach
is superior at estimating more pronounced distortion effects. Our
findings contribute to the robust unsupervised blind estimation of
audio effects, demonstrating the potential of diffusion models for
system identification in music technology.
Download Time-Variant Gray-Box Modeling of a Phaser Pedal A method to measure the response of a linear time-variant (LTV) audio system is presented. The proposed method uses a series of short chirps generated as the impulse response of several cascaded allpass filters. This test signal can measure the characteristics of an LTV system as a function of time. Results obtained from testing of this method on a guitar phaser pedal are presented. A proof of concept gray-box model of the measured system is produced based on partial knowledge about the internal structure of the pedal and on the spectral analysis of the measured responses. The temporal behavior of the digital model is shown to be very similar to that of the measured device. This demonstrates that it is possible to measure LTV analog audio systems and produce approximate virtual analog models based on these results.
Download Rounding Corners with BLAMP The use of the bandlimited ramp (BLAMP) function as an antialiasing tool for audio signals with sharp corners is presented. Discontinuities in the waveform of a signal or its derivatives require infinite bandwidth and are major sources of aliasing in the digital domain. A polynomial correction function is modeled after the ideal BLAMP function. This correction function can be used to treat aliasing caused by sharp edges or corners which translate into discontinuities in the first derivative of a signal. Four examples of cases where these discontinuities appear are discussed: synthesis of triangular waveforms, hard clipping, and half-wave and fullwave rectification. Results obtained show that the BLAMP function is a more efficient tool for alias reduction than oversampling. The polynomial BLAMP can reduce the level of aliasing components by up to 50 dB and improve the overall signal-to-noise ratio by about 20 dB. The proposed method can be incorporated into virtual analog models of musical systems.