Paper Archive

In this paper we present an approach to using traditional digital IIR filter structures inside deep-learning networks trained using backpropagation. We establish the link between such structures and recurrent neural networks. Three different differentiable IIR filter topologies are presented and compared against each other and an established baseline. Additionally, a simple Wiener-Hammerstein model using differentiable IIRs as its filtering component is presented and trained on a guitar signal played through a Boss DS-1 guitar pedal.

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Guitar effects are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. The sound is not only determined by the choice of the guitar effect, but also heavily depends on the parameter settings of the effect. This paper introduces a method to estimate the parameter settings of guitar effects, which makes it possible to reconstruct the effect and its settings from an audio recording of a guitar. The method utilizes audio feature extraction and shallow neural networks, which are trained on data created specifically for this task. The results show that the method is generally suited for this task with average estimation errors of ±5% − ±16% of different parameter scales and could potentially perform near the level of a human expert.

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We explore the use of neural synthesis for acoustic guitar from string-wise MIDI input. We propose four different systems and compare them with both objective metrics and subjective evaluation against natural audio and a sample-based baseline. We iteratively develop these four systems by making various considerations on the architecture and intermediate tasks, such as predicting pitch and loudness control features. We find that formulating the control feature prediction task as a classification task rather than a regression task yields better results. Furthermore, we find that our simplest proposed system, which directly predicts synthesis parameters from MIDI input performs the best out of the four proposed systems. Audio examples and code are available.

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This paper proposes reservoir computing as a general framework for nonlinear audio processing. Reservoir computing is a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It can in theory approximate arbitrary nonlinear dynamical systems with arbitrary precision, has an inherent temporal processing capability and is therefore well suited for many nonlinear audio processing problems. Always when nonlinear relationships are present in the data and time information is crucial, reservoir computing can be applied. Examples from three application areas are presented: nonlinear system identification of a tube amplifier emulator algorithm, nonlinear audio prediction, as necessary in a wireless transmission of audio where dropouts may occur, and automatic melody transcription out of a polyphonic audio stream, as one example from the big field of music information retrieval. Reservoir computing was able to outperform state-of-the-art alternative models in all studied tasks.

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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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Many recent approaches to creative transformations of musical audio have been motivated by the success of raw audio generation models such as WaveNet, in which audio samples are modeled by generative neural networks. This paper describes a generative audio synthesis model for multi-drum translation based on a WaveNet denosing autoencoder architecture. The timbre of an arbitrary source audio input is transformed to sound as if it were played by various percussive instruments while preserving its rhythmic structure. Two evaluations of the transformations are conducted based on the capacity of the model to preserve the rhythmic patterns of the input and the audio quality as it relates to timbre of the target drum domain. The first evaluation measures the rhythmic similarities between the source audio and the corresponding drum translations, and the second provides a numerical analysis of the quality of the synthesised audio. Additionally, a semi- and fully-automatic audio effect has been proposed, in which the user may assist the system by manually labelling source audio segments or use a state-of-the-art automatic drum transcription system prior to drum translation.

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A system is proposed to perform the automatic music transcription of simple polyphonic tracks using top-down processing. It is composed of a blackboard system of three hierarchical levels, receiving its input from a segmentation routine in the form of an averaged STFT matrix. The blackboard contains a hypotheses database, a scheduler and knowledge sources, one of which is a neural network chord recogniser with the ability to reconfigure the operation of the system, allowing it to output more than one note hypothesis at a time. The basic implementation is explained, and some examples are provided to illustrate the performance of the system. The weaknesses of the current implementation are shown and next steps for further development of the system are defined.

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The recently developed extension of Wave Digital Filters based on vector wave variables has broadened the class of circuits with linear two-port elements that can be modeled in a modular and explicit fashion in the Wave Digital (WD) domain. In this paper, we apply the vector definition of wave variables to nonlinear twoport elements. In particular, we present two vector WD models of a Bipolar Junction Transistor (BJT) using characteristic equations derived from an extended Ebers-Moll model. One, implicit, is based on a modified Newton-Raphson method; the other, explicit, is based on a neural network trained in the WD domain and it is shown to allow fully explicit implementation of circuits with a single BJT, which can be executed in real time.

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One of the most challenging tasks in physically-informed sound synthesis is the estimation of model parameters to produce a desired timbre. Automatic parameter estimation procedures have been developed in the past for some specific parameters or application scenarios but, up to now, no approach has been proved applicable to a wide variety of use cases. A general solution to parameters estimation problem is provided along this paper which is based on a supervised convolutional machine learning paradigm. The described approach can be classified as “end-to-end” and requires, thus, no specific knowledge of the model itself. Furthermore, parameters are learned from data generated by the model, requiring no effort in the preparation and labeling of the training dataset. To provide a qualitative and quantitative analysis of the performance, this method is applied to a patented digital waveguide pipe organ model, yielding very promising results.

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In this transformation we present a rhythmically constrained audio style transfer technique for automatic mixing and mashing of two audio inputs. In this transformation the rhythmic and timbral features of both input signals are combined together through the use of an audio style transfer process that transforms the files so that they adhere to a larger metrical structure of the chosen input. This is accomplished by finding beat boundaries of both inputs and performing the transformation on beat-length audio segments. In order for the system to perform a mashup between two signals, we reformulate the previously used audio style transfer loss terms into three loss functions and enable them to be independent of the input. We measure and compare rhythmic similarities of the transformed and input audio signals using their rhythmic envelopes to investigate the influence of the tested transformation objectives.

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