Download Real-Time Audio Visualization With Reassigned Non-uniform Filter Banks
Filter banks, both uniform and non-uniform, are widely used for signal analysis and processing. However, the application of a timefrequency localized filter inevitably causes some amount of spectral and temporal leakage that, simultaneously, cannot be arbitrarily reduced. Reassignment is a classical procedure to eliminate this leakage in short-time Fourier spectrograms, thereby providing a sharper, more exact time-frequency domain signal representation. The reassignment technique was recently generalized to general filter banks, opening new possibilities for its application in signal analysis and processing. We present here the very first implementation of filter bank reassignment in a real-time analysis setting, more specifically as visualization in a basic audio player application. The visualization provides a low delay moving spectrogram with respect to virtually any time-frequency filter bank by interfacing the C backend of the LTFAT open-source toolbox for time-frequency processing. Low delay is achieved by blockwise processing, implemented with the JUCE C++ Library.
Download Estimates of the Reconstruction Error in Partially Redressed Warped Frames Expansions
In recent work, redressed warped frames have been introduced for the analysis and synthesis of audio signals with nonuniform frequency and time resolutions. In these frames, the allocation of frequency bands or time intervals of the elements of the representation can be uniquely described by means of a warping map. Inverse warping applied after time-frequency sampling provides the key to reduce or eliminate dispersion of the warped frame elements in the conjugate variable, making it possible, e.g., to construct frequency warped frames with synchronous time alignment through frequency. The redressing procedure is however exact only when the analysis and synthesis windows have compact support in the domain where warping is applied. This implies that frequency warped frames cannot have compact support in the time domain. This property is undesirable when online computation is required. Approximations in which the time support is finite are however possible, which lead to small reconstruction errors. In this paper we study the approximation error for compactly supported frequency warped analysis-synthesis elements, providing a few examples and case studies.
Download Real-Time Spectrogram Inversion Using Phase Gradient Heap Integration
The knowledge of the phase of STFT is a prerequisite for a successful signal reconstruction. However, the phase might be lost or no longer applicable depending on the kind of processing involved. We propose a real-time spectrogram inversion algorithm based on the relationship of the gradients of the phase and the logarithm of the magnitude and on the gradient integration theorem. We present a detailed comparison with the state-of-the-art phase reconstruction algorithms.
Download Modifying Signals in Transform Domain: a Frame-Based Inverse Problem
Within this paper a method for morphing audio signals is presented. The theory is based on general frames and the modification of the signals is done via frame multiplier. Searching this frame multiplier with given input and output signal, an inverse problem occurs and a priori information is added with regularization terms. A closed-form solution is obtained by a diagonal approximation, i.e. using only the diagonal entries in the signal transformations. The proposed solutions for different regularization terms are applied to Gabor frames and to the constant-Q transform, based on non-stationary Gabor frames.
Download Time-Variant Gray-Box Modeling of a Phaser Pedal
A method to measure the response of a linear time-variant (LTV) audio system is presented. The proposed method uses a series of short chirps generated as the impulse response of several cascaded allpass filters. This test signal can measure the characteristics of an LTV system as a function of time. Results obtained from testing of this method on a guitar phaser pedal are presented. A proof of concept gray-box model of the measured system is produced based on partial knowledge about the internal structure of the pedal and on the spectral analysis of the measured responses. The temporal behavior of the digital model is shown to be very similar to that of the measured device. This demonstrates that it is possible to measure LTV analog audio systems and produce approximate virtual analog models based on these results.
Download Black-box Modeling of Distortion Circuits with Block-Oriented Models
This paper describes black-box modeling of distortion circuits. The analyzed distortion circuits all originate from guitar effect pedals, which are widely used to enrich the sound of an electric guitar with harmonics. The proposed method employs a blockoriented model which consists of a linear block (filter) and a nonlinear block. In this study the nonlinear block is represented by an extended parametric input/output mapping function. Three distortion circuits with different nonlinear elements are analyzed and modeled. The linear and nonlinear parts of the circuit are analyzed and modeled separately. The Levenberg–Marquardt algorithm is used for iterative optimization of the nonlinear parts of the circuits. Some circuits could not be modeled with high accuracy, but the proposed model has shown to be a versatile and flexible tool when modeling distortion circuits.
Download Physical Model Parameter Optimisation for Calibrated Emulation of the Dallas Rangemaster Treble Booster Guitar Pedal
In this work we explore optimising parameters of a physical circuit model relative to input/output measurements, using the Dallas Rangemaster Treble Booster as a case study. A hybrid metaheuristic/gradient descent algorithm is implemented, where the initial parameter sets for the optimisation are informed by nominal values from schematics and datasheets. Sensitivity analysis is used to screen parameters, which informs a study of the optimisation algorithm against model complexity by fixing parameters. The results of the optimisation show a significant increase in the accuracy of model behaviour, but also highlight several key issues regarding the recovery of parameters.
Download Circuit Simulation with Inductors and Transformers Based on the Jiles-Atherton Model of Magnetization
The sound of a vacuum tube guitar amplifier may be significantly influenced by the non-linear behavior of its output transformer, which therefore should also be considered in digital simulations. In this work, we develop a model for inductors and transformers with the magnetization following the model of Jiles and Atherton. For this purpose, the original magnetization model is rewritten to a differential equation with respect to time which can then easily be integrated into a previously developed circuit simulation framework. The model thus derived is then exercised in the simulation of three simple circuits where it shows the expected behavior.
Download A Cosine-Distance Based Neural Network for Music Artist Recognition Using Raw I-Vector Feature
Recently, i-vector features have entered the field of Music Information Retrieval (MIR), exhibiting highly promising performance in important tasks such as music artist recognition or music similarity estimation. The i-vector modelling approach relies on a complex processing chain that limits by the use of engineered features such as MFCCs. The goal of the present paper is to make an important step towards a truly end-to-end modelling system inspired by the i-vector pipeline, to exploit the power of Deep Neural Networks1 (DNNs) to learn optimized feature spaces and transformations. Several authors have already tried to combine the power of DNNs with i-vector features, where DNNs were used for feature extraction, scoring or classification. In this paper, we try to use neural networks for the important step of i-vector post-processing and classification for the task of music artist recognition. Specifically, we propose a novel neural network for i-vector features with a cosine-distance loss function, optimized with stochastic gradient decent (SGD). We first show that current networks do not perform well with unprocessed i-vector features, and that post-processing methods such as Within-Class Covariance Normalization (WCCN) and Linear Discriminant Analysis (LDA) are crucially important to improve the i-vector representation. We further demonstrate that these linear projections (WCCN and LDA) can not be learned using general objective functions usually used in neural networks. We examine our network on a 50-class music artist recognition dataset using i-vectors extracted from frame-level timbre features. Our experiments suggest that using our network with fully unprocessed i-vectors, we can achieve the performance of the i-vector pipeline which uses i-vector post processing methods such as LDA and WCCN.
Download Hubness-Aware Outlier Detection for Music Genre Recognition
Outlier detection is the task of automatic identification of unknown data not covered by training data (e.g. a new genre in genre recognition). We explore outlier detection in the presence of hubs and anti-hubs, i.e. data objects which appear to be either very close or very far from most other data due to a problem of measuring distances in high dimensions. We compare a classic distance based method to two new approaches, which have been designed to counter the negative effects of hubness, on two standard music genre data sets. We demonstrate that anti-hubs are responsible for many detection errors and that this can be improved by using a hubness-aware approach.