Paper Archive

This paper presents a technique addressing signal discontinuity and concatenation artefacts in real-time granular processing with rectangular windowing. By combining zero-crossing synchronicity, first-order derivative analysis, and Lagrange polynomials, we can generate streams of uncorrelated and non-overlapping sonic fragments with minimal low-order derivatives discontinuities. The resulting open-source algorithm, implemented in the Faust language, provides a versatile real-time software for dynamical looping, wavetable oscillation, and granulation with reduced artefacts due to rectangular windowing and no artefacts from overlap-add-to-one techniques commonly deployed in granular processing.

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Contemporary musicians commonly face the challenge of finding new, characteristic sounds that can make their compositions more distinct. They often resort to computers and algorithms, which can significantly aid in creative processes by generating unexpected material in controlled probabilistic processes. In particular, algorithms that present emergent behaviors, like genetic algorithms and cellular automata, have fostered a broad diversity of musical explorations. This article proposes an original technique for the computer-assisted creation and manipulation of sound textures. The technique uses Probabilistic Cellular Automata, which are yet seldom explored in the music domain, to blend two audio tracks into a third, different one. The proposed blending process works by dividing the source tracks into frequency bands and then associating each of the automaton’s cell to a frequency band. Only one source, chosen by the cell’s state, is active within each band. The resulting track has a non-repeating textural pattern that follows the changes in the Cellular Automata. This blending process allows the musician to choose the original material and the blend granularity, significantly changing the resulting blends. We demonstrate how to use the proposed blending process in sound design and its application in experimental and popular music.

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In this paper we describe similarity graphs computed from timefrequency analysis as a guide for audio playback, with the aim of extending the content of fixed recordings in creative applications. We explain the creation of the graph from the distance between spectral frames, as well as several features computed from the graph, such as methods for onset detection, beat detection, and cluster analysis. Several playback algorithms can be devised based on conditional pruning of the graph using these methods. We describe examples for looping, granulation, and automatic montage.

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In recent years, a range of topological methods have emerged for processing digital signals. In this paper we show how the construction of topological filters via sheaves can be used to topologize existing sound synthesis methods. I illustrate this process on two classes of synthesis approaches: (1) based on linear-time invariant digital filters and (2) based on oscillators defined on a circle. We use the computationally-friendly approach to modeling topologies via a simplicial complex, and we attach our classical synthesis methods to them via sheaves. In particular, we explore examples of simplicial topologies that mimic sampled lines and loops. Over these spaces we realize concrete examples of simple discrete harmonic oscillators (resonant filters), and simple comb filter based algorithms (such as Karplus-Strong) as well as frequency modulation.

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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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Deep neural networks have been recently applied to the task of automatic synthesizer programming, i.e., finding optimal values of sound synthesis parameters in order to reproduce a given input sound. This paper focuses on generative models, which can infer parameters as well as generate new sets of parameters or perform smooth morphing effects between sounds. We introduce new models to ensure scalability and to increase performance by using heterogeneous representations of parameters as numerical and categorical random variables. Moreover, a spectral variational autoencoder architecture with multi-channel input is proposed in order to improve inference of parameters related to the pitch and intensity of input sounds. Model performance was evaluated according to several criteria such as parameters estimation error and audio reconstruction accuracy. Training and evaluation were performed using a 30k presets dataset which is published with this paper. They demonstrate significant improvements in terms of parameter inference and audio accuracy and show that presented models can be used with subsets or full sets of synthesizer parameters.

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Virtual analog (VA) modeling using neural networks (NNs) has great potential for rapidly producing high-fidelity models. Recurrent neural networks (RNNs) are especially appealing for VA due to their connection with discrete nodal analysis. Furthermore, VA models based on NNs can be trained efficiently by directly exposing them to the circuit states in a gray-box fashion. However, exposure to ground truth information during training can leave the models susceptible to error accumulation in a free-running mode, also known as “exposure bias” in machine learning literature. This paper presents a unified framework for treating the previously proposed state trajectory network (STN) and gated recurrent unit (GRU) networks as special cases of discrete nodal analysis. We propose a novel circuit state-matching mechanism for the GRU and experimentally compare the previously mentioned networks for their performance in state matching, during training, and in exposure bias, during inference. Experimental results from modeling a diode clipper show that all the tested models exhibit some exposure bias, which can be mitigated by truncated backpropagation through time. Furthermore, the proposed state matching mechanism improves the GRU modeling performance of an overdrive pedal and a phaser pedal, especially in the presence of external modulation, apparent in a phaser circuit.

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Piano transcription is a classic problem in music information retrieval. More and more transcription methods based on deep learning have been proposed in recent years. In 2019, Google Brain published a larger piano transcription dataset, MAESTRO. On this dataset, Onsets and Frames transcription approach proposed by Hawthorne achieved a stunning onset F1 score of 94.73%. Unlike the annotation method of Onsets and Frames, Transition-aware model presented in this paper annotates the attack process of piano signals called atack transition in multiple frames, instead of only marking the onset frame. In this way, the piano signals around onset time are taken into account, enabling the detection of piano onset more stable and robust. Transition-aware achieves a higher transcription F1 score than Onsets and Frames on MAESTRO dataset and MAPS dataset, reducing many extra note detection errors. This indicates that Transition-aware approach has better generalization ability on different datasets.

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It is difficult to adjust the parameters of a complex synthesizer to create the desired sound. As such, sound matching, the estimation of synthesis parameters that can replicate a certain sound, is a task that has often been researched, utilizing optimization methods such as genetic algorithm (GA). In this paper, we introduce a novelty-based objective for GA-based sound matching. Our contribution is two-fold. First, we show that the novelty objective is able to improve the quality of sound matching by maintaining phenotypic diversity in the population. Second, we introduce a quality diversity approach to the problem of sound matching, aiming to find a diverse set of matching sounds. We show that the novelty objective is effective in producing high-performing solutions that are diverse in terms of specified audio features. This approach allows for a new way of discovering sounds and exploring the capabilities of a synthesizer.

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Piano transcription is a fundamental problem in the field of music information retrieval. At present, a large number of transcriptional studies are mainly based on audio or video, yet there is a small number of discussion based on audio-visual fusion. In this paper, a piano transcription model based on strategy fusion is proposed, in which the transcription results of the video model are used to assist audio transcription. Due to the lack of datasets currently used for audio-visual fusion, the OMAPS data set is proposed in this paper. Meanwhile, our strategy fusion model achieves a 92.07% F1 score on OMAPS dataset. The transcription model based on feature fusion is also compared with the one based on strategy fusion. The experiment results show that the transcription model based on strategy fusion achieves better results than the one based on feature fusion.

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