Paper Archive

Real-time applications of Music Information Retrieval (MIR) have been gaining interest as of recently. However, as deep learning becomes more and more ubiquitous for music analysis tasks, several challenges and limitations need to be overcome to deliver accurate and quick real-time MIR systems. In addition, modern embedded computers offer great potential for compact systems that use MIR algorithms, such as digital musical instruments. However, embedded computing hardware is generally resource constrained, posing additional limitations. In this paper, we identify and discuss the challenges and limitations of embedded real-time MIR. Furthermore, we discuss potential solutions to these challenges, and demonstrate their validity by presenting an embedded real-time classifier of expressive acoustic guitar techniques. The classifier achieved 99.2% accuracy in distinguishing pitched and percussive techniques and a 99.1% average accuracy in distinguishing four distinct percussive techniques with a fifth class for pitched sounds. The full classification task is a considerably more complex learning problem, with our preliminary results reaching only 56.5% accuracy. The results were produced with an average latency of 30.7 ms.

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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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This work aims to implement a novel deep learning architecture to perform audio processing in the context of matched equalization. Most existing methods for automatic and matched equalization show effective performance and their goal is to find a respective transfer function given a frequency response. Nevertheless, these procedures require a prior knowledge of the type of filters to be modeled. In addition, fixed filter bank architectures are required in automatic mixing contexts. Based on end-to-end convolutional neural networks, we introduce a general purpose architecture for equalization matching. Thus, by using an end-toend learning approach, the model approximates the equalization target as a content-based transformation without directly finding the transfer function. The network learns how to process the audio directly in order to match the equalized target audio. We train the network through unsupervised and supervised learning procedures. We analyze what the model is actually learning and how the given task is accomplished. We show the model performing matched equalization for shelving, peaking, lowpass and highpass IIR and FIR equalizers.

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A filtering algorithm for generating subtle random variations in sampled sounds is proposed. Using only one recording for impact sound effects or drum machine sounds results in unrealistic repetitiveness during consecutive playback. This paper studies spectral variations in repeated knocking sounds and in three drum sounds: a hihat, a snare, and a tomtom. The proposed method uses a short pseudo-random velvet-noise filter and a low-shelf filter to produce timbral variations targeted at appropriate spectral regions, yielding potentially an endless number of new realistic versions of a single percussive sampled sound. The realism of the resulting processed sounds is studied in a listening test. The results show that the sound quality obtained with the proposed algorithm is at least as good as that of a previous method while using 77% fewer computational operations. The algorithm is widely applicable to computer-generated music and game audio.

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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.

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We address Task A of the 1st DAFx Parameter Estimation Challenge, which aims to retrieve the physical parameters of a plate model from an impulse response. To do so, we use the Simulation-Based Inference (SBI) framework, in which we train a neural network to estimate a density over plate parameters given an impulse response, using a dataset generated by the simulator. Inference for a new impulse response then requires only a forward pass through the network, without involving the simulator. For each test observation, we fine-tune a specific network: additional simulation rounds are performed by sampling parameters from the current estimated distribution, simulating the corresponding impulse responses, and fine-tuning to produce the specialized network.

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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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Virtual analog (VA) modeling methods seek to emulate analog audio hardware using digital signal processing (DSP). Modeling approaches fall into three broad categories: white-box methods, which use detailed device knowledge for accurate simulation; gray-box methods that use generic DSP blocks to model the system; and black-box methods, which rely solely on opaque models learned from input–output data. A category of architectures used widely in black-box modeling are recurrent neural networks (RNNs). To model device controls, the control values can be provided as conditioning input to the network. However, when the conditioning is time-varied, the models are susceptible to producing noise artifacts. Regularization of the RNN dynamics significantly reduces these artifacts, though at a loss in modeling accuracy. This paper closes the dynamics regularization quality gap by introducing deep control-conditioned LSTMs and a gammatone filterbank (GFB) loss. Experiments indicate that the proposed method achieves comparable modeling performance as unregularized baselines while avoiding the noise artifacts caused by time-varying control inputs.

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Deep learning models have provided extremely successful solutions in most audio application fields. However, the high accuracy of these models comes at the expense of a tremendous computation cost. This aspect is almost always overlooked in evaluating the quality of proposed models. However, models should not be evaluated without taking into account their complexity. This aspect is especially critical in audio applications, which heavily relies on specialized embedded hardware with real-time constraints. In this paper, we build on recent observations that deep models are highly overparameterized, by studying the lottery ticket hypothesis on deep generative audio models. This hypothesis states that extremely efficient small sub-networks exist in deep models and would provide higher accuracy than larger models if trained in isolation. However, lottery tickets are found by relying on unstructured masking, which means that resulting models do not provide any gain in either disk size or inference time. Instead, we develop here a method aimed at performing structured trimming. We show that this requires to rely on global selection and introduce a specific criterion based on mutual information. First, we confirm the surprising result that smaller models provide higher accuracy than their large counterparts. We further show that we can remove up to 95% of the model weights without significant degradation in accuracy. Hence, we can obtain very light models for generative audio across popular methods such as Wavenet, SING or DDSP, that are up to 100 times smaller with commensurate accuracy. We study the theoretical bounds for embedding these models on Raspberry Pi and Arduino, and show that we can obtain generative models on CPU with equivalent quality as large GPU models. Finally, we discuss the possibility of implementing deep generative audio models on embedded platforms.

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We release Amp-Space, a large-scale dataset of paired audio samples: a source audio signal, and an output signal, the result of a timbre transformation. The types of transformations we study are from blackbox musical tools (amplifiers, stompboxes, studio effects) traditionally used to shape the sound of guitar, bass, or synthesizer sounds. For each sample of transformed audio, the set of parameters used to create it are given. Samples are from both real and simulated devices, the latter allowing for orders of magnitude greater data than found in comparable datasets. We demonstrate potential use cases of this data by (a) pre-training a conditional WaveNet model on synthetic data and show that it reduces the number of samples necessary to digitally reproduce a real musical device, and (b) training a variational autoencoder to shape a continuous space of timbre transformations for creating new sounds through interpolation.

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