Paper Archive

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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Automatic detection of musical patterns is an important task in the field of Music Information Retrieval due to its usage in multiple applications such as automatic music transcription, genre or instrument identification, music classification, and music recommendation. A significant sub-task in pattern detection is the realtime pattern detection in music due to its relevance in application domains such as the Internet of Musical Things. In this study, we present a method to identify the occurrence of known patterns in symbolic monophonic music streams in real-time. We introduce a matrix-based representation to denote musical notes using its pitch, pitch-bend, amplitude, and duration. We propose an algorithm based on an independent similarity index for each note attribute. We also introduce the Match Measure, which is a numerical value signifying the degree of the match between a pattern and a sequence of notes. We have tested the proposed algorithm against three datasets: a human recorded dataset, a synthetically designed dataset, and the JKUPDD dataset. Overall, a detection rate of 95% was achieved. The low computational load and minimal running time demonstrate the suitability of the method for real-world, real-time implementations on embedded systems.

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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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Recent advancements in deep learning have shown great potential for audio applications, improving the accuracy of previous solutions for tasks such as music transcription, beat detection, and real-time audio processing. In addition, the availability of increasingly powerful embedded computers has led many deep learning framework developers to devise software optimized to run pretrained models in resource-constrained contexts. As a result, the use of deep learning on embedded devices and audio plugins has become more widespread. However, confusion has been rising around deep learning inference engines, regarding which of these can run in real-time and which are less resource-hungry. In this paper, we present a comparison of four available deep learning inference engines for real-time audio classification on the CPU of an embedded single-board computer: TensorFlow Lite, TorchScript, ONNX Runtime, and RTNeural. Results show that all inference engines can execute neural network models in real-time with appropriate code practices, but execution time varies between engines and models. Most importantly, we found that most of the less-specialized engines offer great flexibility and can be used effectively for real-time audio classification, with slightly better results than a real-time-specific approach. In contrast, more specialized solutions can offer a lightweight and minimalist alternative where less flexibility is needed.

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Recent studies on classifying electric guitar effects have achieved high accuracy, particularly with deep learning techniques. However, these studies often rely on simplified datasets consisting mainly of single notes rather than realistic guitar recordings. Moreover, in the specific field of effect chain estimation, the literature tends to rely on large models, making them impractical for real-time or resource-constrained applications. In this work, we recorded realistic guitar performances using four different guitars and created three datasets by applying a chain of five effects with increasing complexity: (1) fixed order and parameters, (2) fixed order with randomly sampled parameters, and (3) random order and parameters. We also propose a novel Neural Architecture Search method aimed at discovering accurate yet compact convolutional neural network models to reduce power and memory consumption. We compared its performance to a basic random search strategy, showing that our custom Neural Architecture Search outperformed random search in identifying models that balance accuracy and complexity. We found that the number of convolutional and pooling layers becomes increasingly important as dataset complexity grows, while dense layers have less impact. Additionally, among the effects, tremolo was identified as the most challenging to classify.

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To date, the most successful onset detectors are those based on frequency representation of the signal. However, for such methods the time between the physical onset and the reported one is unpredictable and may largely vary according to the type of sound being analyzed. Such variability and unpredictability of spectrum-based onset detectors may not be convenient in some real-time applications. This paper proposes a real-time method to improve the temporal accuracy of state-of-the-art onset detectors. The method is grounded on the theory of hard real-time operating systems where the result of a task must be reported at a certain deadline. It consists of the combination of a time-base technique (which has a high degree of accuracy in detecting the physical onset time but is more prone to false positives and false negatives) with a spectrum-based technique (which has a high detection accuracy but a low temporal accuracy). The developed hard real-time onset detector was tested on a dataset of single non-pitched percussive sounds using the high frequency content detector as spectral technique. Experimental validation showed that the proposed approach was effective in better retrieving the physical onset time of about 50% of the hits detected by the spectral technique, with an average improvement of about 3 ms and maximum one of about 12 ms. The results also revealed that the use of a longer deadline may capture better the variability of the spectral technique, but at the cost of a bigger latency.

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Onset detectors are used to recognize the beginning of musical events in audio signals. Manual parameter tuning for onset detectors is a time consuming task, while existing automated approaches often maximize only a single performance metric. These automated approaches cannot be used to optimize detector algorithms for complex scenarios, such as real-time onset detection where an optimization process must consider both detection accuracy and latency. For this reason, a flexible optimization algorithm should account for more than one performance metric in a multiobjective manner. This paper presents a generalized procedure for automated optimization of parametric onset detectors. Our procedure employs a bio-inspired evolutionary computation algorithm to replace manual parameter tuning, followed by the computation of the Pareto frontier for multi-objective optimization. The proposed approach was evaluated on all the onset detection methods of the Aubio library, using a dataset of monophonic acoustic guitar recordings. Results show that the proposed solution is effective in reducing the human effort required in the optimization process: it replaced more than two days of manual parameter tuning with 13 hours and 34 minutes of automated computation. Moreover, the resulting performance was comparable to that obtained by manual optimization.

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We describe a system to synthesize in real-time footsteps sounds. The sound engine is based on physical models and physically inspired models reproducing the act of walking on several surfaces. To control the real-time engine, three solutions are proposed. The first two solutions are based on floor microphones, while the third one is based on shoes enhanced with sensors. The different solutions proposed are discussed in the paper.

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