Paper Archive

This study investigates the potential of real-time inference of neural audio effects on Android smartphones, marking an initial step towards bridging the gap in neural audio processing for mobile devices. Focusing exclusively on processing rather than synthesis, we explore the performance of three open-source neural models across five Android phones released between 2014 and 2022, showcasing varied capabilities due to their generational differences. Through comparative analysis utilizing two C++ inference engines (ONNX Runtime and RTNeural), we aim to evaluate the computational efficiency and timing performance of these models, considering the varying computational loads and the hardware specifics of each device. Our work contributes insights into the feasibility of implementing neural audio processing in real-time on mobile platforms, highlighting challenges and opportunities for future advancements in this rapidly evolving field.

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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.

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This paper introduces the possibilities of optimizing neural network convolutional layers for modeling nonlinear audio systems and effects. Enhanced methods for real-time dilated convolutions are presented to achieve faster signal processing times than in previous work. Due to the improved implementation of convolutional layers, a significant decrease in computational requirements was observed and validated on different configurations of single layers with dilated convolutions and WaveNet-style feedforward neural network models. In most cases, equivalent signal processing times were achieved to those using recurrent neural networks with Long Short-Term Memory units and Gated Recurrent Units, which are considered state-of-the-art in the field of black-box virtual analog modeling.

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Removing audio effects from electric guitar recordings makes it easier for post-production and sound editing. An audio distortion recovery model not only improves the clarity of the guitar sounds but also opens up new opportunities for creative adjustments in mixing and mastering. While progress have been made in creating such models, previous efforts have largely focused on synthetic distortions that may be too simplistic to accurately capture the complexities seen in real-world recordings. In this paper, we tackle the task by using a dataset of guitar recordings rendered with commercial-grade audio effect VST plugins. Moreover, we introduce a novel two-stage methodology for audio distortion recovery. The idea is to firstly process the audio signal in the Mel-spectrogram domain in the first stage, and then use a neural vocoder to generate the pristine original guitar sound from the processed Mel-spectrogram in the second stage. We report a set of experiments demonstrating the effectiveness of our approach over existing methods, through both subjective and objective evaluation metrics.

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We present a new approach for audio bandwidth extension for music signals using convolutional neural networks (CNNs). Inspired by the concept of inpainting from the field of image processing, we seek to reconstruct the high-frequency region (i.e., above a cutoff frequency) of a time-frequency representation given the observation of a band-limited version. We then invert this reconstructed time-frequency representation using the phase information from the band-limited input to provide an enhanced musical output. We contrast the performance of two musically adapted CNN architectures which are trained separately using the STFT and the invertible CQT. Through our evaluation, we demonstrate that the CQT, with its logarithmic frequency spacing, provides better reconstruction performance as measured by the signal to distortion ratio.

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In this paper, we present an approach to the neural modeling of overdrive guitar pedals with conditioning from a cross-circuit and cross-setting latent space. The resulting network models the behavior of multiple overdrive pedals across different settings, offering continuous morphing between real configurations and hybrid behaviors. Compact conditioning spaces are obtained through unsupervised training of a variational autoencoder with adversarial training, resulting in accurate reconstruction performance across different sets of pedals. We then compare three Hyper-Recurrent architectures for processing, including dynamic and static HyperRNNs, and a smaller model for real-time processing. Additionally, we present pOD-set, a new open dataset including recordings of 27 analog overdrive pedals, each with 36 gain and tone parameter combinations totaling over 97 hours of recordings. Precise parameter setting was achieved through a custom-deployed recording robot.

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The success recently encountered by physically-based modeling (or model-based approaches) for music should not mask the deep challenges that remain in this area. This article first proposes an overview of the various goals that researchers and musicians, respectively operating from scientific and end-user perspectives, may pursue. Among these goals, those recently proposed or particularly critical for the coming years of research are highlighted. The article then introduces ten criteria that summarize the main features an optimal physically-based modeling scheme or language should present. With respect to these, it proposes an evaluation of the major approaches to physically-based modeling. Key words: goals of the physically-based approach to sound synthesis and music creation, languages and schemes, enduser needs, perception, evaluation criteria, bibliographic overview.

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Structured pruning is a technique for reducing the computational load and memory footprint of neural networks by removing structured subsets of parameters according to a predefined schedule or ranking criterion. This paper investigates the application of structured pruning to real-time neural network audio effects, focusing on both feedforward networks and recurrent architectures. We evaluate multiple pruning strategies at inference time, without retraining, and analyze their effects on model performance. To quantify the trade-off between parameter count and audio fidelity, we construct a theoretical model of the approximation error as a function of network architecture and pruning level. The resulting bounds establish a principled relationship between pruninginduced sparsity and functional error, enabling informed deployment of neural audio effects in constrained real-time environments.

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The dance music genres of hardcore, jungle and drum & bass (HJDB) emerged in the United Kingdom during the early 1990s as a result of affordable consumer sampling technology and the popularity of rave music and culture. A key attribute of these genres is their usage of fast-paced drums known as breakbeats. Automated analysis of breakbeat usage in HJDB would allow for novel digital audio effects and musicological investigation of the genres. An obstacle in this regard is the automated identification of breakbeats used in HJDB music. This paper compares three strategies for breakbeat detection: (1) a generalised frame-based music classification scheme; (2) a specialised system that segments drums from the audio signal and labels them with an SVM classifier; (3) an alternative specialised approach using a deep network classifier. The results of our evaluations demonstrate the superiority of the specialised approaches, and highlight the need for style-specific workflows in the determination of particular musical attributes in idiosyncratic genres. We then leverage the output of the breakbeat classification system to produce an automated breakbeat sequence reconstruction, ultimately recreating the HJDB percussion arrangement.

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Deep learning models are mostly used in an offline inference fashion. However, this strongly limits the use of these models inside audio generation setups, as most creative workflows are based on real-time digital signal processing. Although approaches based on recurrent networks can be naturally adapted to this buffer-based computation, the use of convolutions still poses some serious challenges. To tackle this issue, the use of causal streaming convolutions have been proposed. However, this requires specific complexified training and can impact the resulting audio quality. In this paper, we introduce a new method allowing to produce non-causal streaming models. This allows to make any convolutional model compatible with real-time buffer-based processing. As our method is based on a post-training reconfiguration of the model, we show that it is able to transform models trained without causal constraints into streaming models. We apply our method on the recent RAVE model as a case study. This model provides high-quality real-time audio synthesis on a wide range of signals and thus is an ideal candidate to evaluate our method. It should be noted that our method is not restricted to RAVE, and can be straightforwardly applied to any convolutional network. We test our approach on multiple music and speech datasets and show that it is faster than overlap-add methods, while having no impact on the generation quality. Finally, we introduce two open-source implementation of our work as Max/MSP and PureData externals, and as a VST audio plugin. This allows to endow traditional digital audio workstations with real-time neural audio synthesis.

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