Paper Archive

Machine learning models have become ubiquitous in modeling analog audio devices. Expanding on this line of research, our study focuses on Voltage-Controlled Oscillators of analog synthesizers. We employ black box autoregressive artificial neural networks to model the typical analog waveshapes, including triangle, square, and sawtooth. The models can be conditioned on wave frequency and type, enabling the generation of pitch envelopes and morphing across waveshapes. We conduct evaluations on both synthetic and analog datasets to assess the accuracy of various architectural variants. The LSTM variant performed better, although lower frequency ranges present particular challenges.

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Virtual Analog (VA) modeling aims to simulate the behavior of hardware circuits via algorithms to replicate their tone digitally. Dynamic Range Compressor (DRC) is an audio processing module that controls the dynamics of a track by reducing and amplifying the volumes of loud and quiet sounds, which is essential in music production. In recent years, neural-network-based VA modeling has shown great potential in producing high-fidelity models. However, due to the lack of data quantity and diversity, their generalization ability in different parameter settings and input sounds is still limited. To tackle this problem, we present Solid State Bus-Comp, the first large-scale and diverse dataset for modeling the classical VCA compressor — SSL 500 G-Bus. Specifically, we manually collected 175 unmastered songs from the Cambridge Multitrack Library. We recorded the compressed audio in 220 parameter combinations, resulting in an extensive 2528-hour dataset with diverse genres, instruments, tempos, and keys. Moreover, to facilitate the use of our proposed dataset, we conducted benchmark experiments in various open-sourced black-box and grey-box models, as well as white-box plugins. We also conducted ablation studies in different data subsets to illustrate the effectiveness of the improved data diversity and quantity. The dataset and demos are on our project page: https: //www.yichenggu.com/SolidStateBusComp/.

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Some early analog voltage-controlled amplifiers (VCAs) utilized semiconductor diodes as a variable-gain element. Diode-based VCAs exhibit a unique sound quality, with distortion dependent both on signal level and gain control. In this work, we examine the behavior of a simplified circuit for a diode-based VCA and propose a nonlinear, explicit, stateless digital model. This approach avoids traditional iterative algorithms, which can be computationally intensive. The resulting digital model retains the sonic characteristics of the analog model and is suitable for real-time simulation. We present an analysis of the gain characteristics and harmonic distortion produced by this model, as well as practical guidance for implementation. We apply this approach to a set of alternative analog topologies and introduce a family of digital VCA models based on fixed nonlinearities with variable operating points.

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Several methods exist in the literature to accurately simulate Bucket Brigade Device (BBD) chips, which are widely used in analog delay-based audio effects for their characteristic lo-fi sound, which is affected by noise, nonlinearities and aliasing. The latter is a desired quality, being typical of those chips. However, when simulating BBDs in a discrete-time domain environment, additional aliasing components occur that need to be suppressed. In this work, we propose a novel method that applies the Bandlimited Step (BLEP) technique, effectively minimizing aliasing artifacts introduced by the simulation. The paper provides some insights on the design of a BBD simulation using interpolation at the input for clock rate conversion and, most importantly, shows how BLEP can be effective in reducing unwanted aliasing artifacts. Interpolation is shown to have minor importance in the reduction of spurious components.

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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.

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In this paper, we propose the use of real-valued Linear Recurrent Units (LRUs) for black-box modeling of audio circuits. A network architecture composed of real LRU blocks interleaved with nonlinear processing stages is proposed. Two case studies are presented, a second-order diode clipper and an overdrive distortion pedal. Furthermore, we show how to integrate the antiderivative antialiaisng technique into the proposed method, effectively lowering oversampling requirements. Our experiments show that the proposed method generates models that accurately capture the nonlinear dynamics of the examined devices and are highly efficient, which makes them suitable for real-time operation inside Digital Audio Workstations.

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Neural networks have been applied within the Wave Digital Filter (WDF) framework as data-driven models for nonlinear multi-port circuit elements. Conventionally, these models are trained on wave variables obtained by sampling the current-voltage characteristic of the considered nonlinear element before being incorporated into the circuit WDF implementation. However, isolating multi-port elements for this process can be challenging, as their nonlinear behavior often depends on dynamic effects that emerge from interactions with the surrounding circuit. In this paper, we propose a novel approach for training neural models of nonlinear multi-port elements directly within a circuit’s Wave Digital (WD) discretetime implementation, relying solely on circuit input-output voltage measurements. Exploiting the differentiability of WD simulations, we embed the neural network into the simulation process and optimize its parameters using gradient-based methods by minimizing a loss function defined over the circuit output voltage. Experimental results demonstrate the effectiveness of the proposed approach in accurately capturing the nonlinear circuit behavior, while preserving the interpretability and modularity of WDFs.

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Recently, energy quadratization and modal expansion have become popular methods for developing efficient physics-based sound synthesis algorithms. These methods have been primarily used to derive explicit schemes modeling the collision between a string and a fixed barrier. In this paper, these techniques are applied to a similar problem: modeling a distributed mouthpiece lay-reed-lip interaction in a woodwind instrument. The proposed model aims to provide a more accurate representation of how a musician’s embouchure affects the reed’s dynamics. The mouthpiece and lip are modeled as distributed static and dynamic viscoelastic barriers, respectively. The reed is modeled using an approximate modal expansion derived via the Rayleigh-Ritz method. The reed system is then acoustically coupled to a measured input impedance response of a saxophone. Numerical experiments are presented.

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From the exploration of databases of instrument sounds to the selfassisted practice of musical instruments, methods for automatically and objectively assessing the quality of musical tones are in high demand. In this paper, we develop a new algorithm for estimating the duration of the attack, with particular attention to wind and bowed string instruments. In fact, for these instruments, the quality of the tones is highly influenced by the attack clarity, for which, together with pitch stability, the attack duration is an indicator often used by teachers by ear. Since the direct estimation of the attack duration from sounds is made difficult by the initial preponderance of the excitation noise, we propose a more robust approach based on the separation of the ensemble of the harmonics from the excitation noise, which is obtained by means of an improved pitchsynchronous wavelet transform. We also define a new parameter, the noise ducking time, which is relevant for detecting the extent of the noise component in the attack. In addition to the exploration of available sound databases, for testing our algorithm, we created an annotated data set in which several problematic sounds are included. Moreover, to check the consistency and robustness of our duration estimates, we applied our algorithm to sets of synthetic sounds with noisy attacks of programmable duration.

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For their low and constant computational cost, non-iterative methods for the solution of differential problems are gaining popularity in virtual analog provided their stability properties and accuracy level afford their use at no exaggerate temporal oversampling. At least in some application case studies, one recent family of noniterative schemes has shown promise to outperform methods that achieve accurate results at the cost of iterating several times while converging to the numerical solution. Here, this family is contextualized and studied against known classes of non-iterative methods. The results from these studies foster a more general discussion about the possibilities, role and prospective use of non-iterative methods in virtual analog.

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