Download Neural Net Tube Models for Wave Digital Filters Herein, we demonstrate the use of neural nets towards simulating multiport nonlinearities inside a wave digital filter. We introduce a resolved wave definition which allows us to extract features from a Kirchhoff domain dataset and train our neural networks directly in the wave domain. A hyperparameter search is performed to minimize error and runtime complexity. To illustrate the method, we model a tube amplifier circuit inspired by the preamplifier stage of the Fender Pro-Junior guitar amplifier. We analyze the performance of our neural nets models by comparing their distortion characteristics and transconductances. Our results suggest that activation function selection has a significant effect on the distortion characteristic created by the neural net.
Download Aliasing Reduction in Neural Amp Modeling by Smoothing Activations The increasing demand for high-quality digital emulations of analog audio hardware, such as vintage tube guitar amplifiers, led
to numerous works on neural network-based black-box modeling,
with deep learning architectures like WaveNet showing promising
results. However, a key limitation in all of these models was the
aliasing artifacts stemming from nonlinear activation functions in
neural networks. In this paper, we investigated novel and modified activation functions aimed at mitigating aliasing within neural
amplifier models. Supporting this, we introduced a novel metric,
the Aliasing-to-Signal Ratio (ASR), which quantitatively assesses
the level of aliasing with high accuracy. Measuring also the conventional Error-to-Signal Ratio (ESR), we conducted studies on a
range of preexisting and modern activation functions with varying
stretch factors. Our findings confirmed that activation functions
with smoother curves tend to achieve lower ASR values, indicating a noticeable reduction in aliasing. Notably, this improvement
in aliasing reduction was achievable without a substantial increase
in ESR, demonstrating the potential for high modeling accuracy
with reduced aliasing in neural amp models.
Download Real-time Pitch Tracking in Audio Signals with the Extended Complex Kalman Filter The Kalman filter is a well-known tool used extensively in robotics, navigation, speech enhancement and finance. In this paper, we propose a novel pitch follower based on the Extended Complex Kalman Filter (ECKF). An advantage of this pitch follower is that it operates on a sample-by-sample basis, unlike other block-based algorithms that are most commonly used in pitch estimation. Thus, it estimates sample-synchronous fundamental frequency (assumed to be the perceived pitch), which makes it ideal for real-time implementation. Simultaneously, the ECKF also tracks the amplitude envelope of the input audio signal. Finally, we test our ECKF pitch detector on a number of cello and double bass recordings played with various ornaments, such as vibrato, portamento and trill, and compare its result with the well-known YIN estimator, to conclude the effectiveness of our algorithm.
Download Bayesian Identification of Closely-Spaced Chords from Single-Frame STFT Peaks Identifying chords and related musical attributes from digital audio has proven a long-standing problem spanning many decades of research. A robust identification may facilitate automatic transcription, semantic indexing, polyphonic source separation and other emerging applications. To this end, we develop a Bayesian inference engine operating on single-frame STFT peaks. Peak likelihoods conditional on pitch component information are evaluated by an MCMC approach accounting for overlapping harmonics as well as undetected/spurious peaks, thus facilitating operation in noisy environments at very low computational cost. Our inference engine evaluates posterior probabilities of musical attributes such as root, chroma (including inversion), octave and tuning, given STFT peak frequency and amplitude observations. The resultant posteriors become highly concentrated around the correct attributes, as demonstrated using 227 ms piano recordings with −10 dB additive white Gaussian noise.
Download The Sounds of the Avian Syrinx - are they Really Flute-Like? This research presents a model of the avian vocal tract, implemented using classical waveguide synthesis and numerical methods. The vocal organ of the songbird, the syrinx, has a unique topography of acoustic tubes (a trachea with a bifurcation at its base) making it a rather unique subject for waveguide synthesis. In the upper region of the two bifid bronchi lies a nonlinear vibrating membrane – the primary resonator in sound production. Unlike most reed musical instruments, the more significant displacement of the membrane is perpendicular to the directions of airflow, due to the Bernoulli effect. The model of the membrane displacement, and the resulting pressure through the constriction created by the membrane motion, is therefore derived beginning with the Bernoulli equation.