Download Model Bending: Teaching Circuit Models New Tricks A technique is introduced for generating novel signal processing systems grounded in analog electronic circuits, called model bending. By applying the ideas behind circuit bending to models of nonlinear analog circuits it is possible to create novel nonlinear signal processors which mimic the behavior of analog electronics, but which are not possible to implement in the analog realm. The history of both circuit bending and circuit modeling is discussed, as well as a theoretical basis for how these approaches can complement each other. Potential pitfalls to the practical application of model bending are highlighted and suggested solutions to those problems are provided, with examples.
Download Equalizing Loudspeakers in Reverberant Environments Using Deep Convolutive Dereverberation Loudspeaker equalization is an established topic in the literature, and currently many techniques are available to address most practical use cases. However, most of these rely on accurate measurements of the loudspeaker in an anechoic environment, which in some occurrences is not feasible. This is the case, e.g. of custom digital organs, which have a set of loudspeakers that are built into a large and geometrically-complex piece of furniture, which may be too heavy and large to be transported to a measurement room, or may require a big one, making traditional impulse response measurements impractical for most users. In this work we propose a method to find the inverse of the sound emission system in a reverberant environment, based on a Deep Learning dereverberation algorithm. The method is agnostic of the room characteristics and can be, thus, conducted in an automated fashion in any environment. A real use case is discussed and results are provided, showing the effectiveness of the approach in designing filters that match closely the magnitude response of the ideal inverting filters.
Download HRTF Spatial Upsampling in the Spherical Harmonics Domain Employing a Generative Adversarial Network A Head-Related Transfer Function (HRTF) is able to capture alterations a sound wave undergoes from its source before it reaches the entrances of a listener’s left and right ear canals, and is imperative for creating immersive experiences in virtual and augmented reality (VR/AR). Nevertheless, creating personalized HRTFs demands sophisticated equipment and is hindered by time-consuming data acquisition processes. To counteract these challenges, various techniques for HRTF interpolation and up-sampling have been proposed. This paper illustrates how Generative Adversarial Networks (GANs) can be applied to HRTF data upsampling in the spherical harmonics domain. We propose using Autoencoding Generative Adversarial Networks (AE-GAN) to upsample lowdegree spherical harmonics coefficients and get a more accurate representation of the full HRTF set. The proposed method is benchmarked against two baselines: barycentric interpolation and HRTF selection. Results from log-spectral distortion (LSD) evaluation suggest that the proposed AE-GAN has significant potential for upsampling very sparse HRTFs, achieving 17% improvement over baseline methods.
Download Segregation of Two Simultaneously Arriving Narrowband Noise Signals as a Function of Spatial and Frequency Separation The present paper details a set of subjective measurements that were carried out in order to investigate the perceptual fusion and segregation of two simultaneously presented ERB-bandlimited noise samples as a function of their frequency separation and difference in the direction of arrival. This research was motivated by the desire to gain insight to virtual source technology in multichannel listening and virtual acoustics applications. The segregation threshold was measured in three different spatial configurations, namely with a 0◦ , a 22.5◦ , or a 45◦ azimuth separation between the two noise signals. The tests were arranged so that the subjects adjusted the frequency gap between the two noise bands until they in their opinion were at the threshold of hearing two separate sounds. The results indicate that the frequency separation threshold is increased above approximately 1.5 kHz. The effect of angle separation between ERB-bands was less significant. It is therefore assumed that the results can be accounted by the loss of accuracy in the neural analysis of the complex stimulus waveform fine structure. The results are also relatively divergent between subjects. This is believed to indicate that sound fusion is an individual concept and partly utilizes higher-level processing.
Download Nicht-negativeMatrixFaktorisierungnutzendes-KlangsynthesenSystem (NiMFKS): Extensions of NMF-based Concatenative Sound Synthesis Concatenative sound synthesis (CSS) entails synthesising a “target” sound with other sounds collected in a “corpus.” Recent work explores CSS using non-negative matrix factorisation (NMF) to approximate a target sonogram by the product of a corpus sonogram and an activation matrix. In this paper, we propose a number of extensions of NMF-based CSS and present an open MATLAB implementation in a GUI-based application we name NiMFKS. In particular we consider the following extensions: 1) we extend the NMF framework by implementing update rules based on the generalised β-divergence; 2) We add an optional monotonic algorithm for sparse-NMF; 3) we tackle the computational challenges of scaling to big corpora by implementing a corpus pruning preprocessing step; 4) we generalise constraints that may be applied to the activation matrix shape; and 5) we implement new modes of interacting with the procedure by enabling sketching and modifying of the activation matrix. Our application, NiMFKS and source code can be downloaded from here: https: //code.soundsoftware.ac.uk/projects/nimfks.
Download DAFx Challenge Introduction & Results The 1st DAFx Parameter Estimation Challenge is an open initiative to advance the state of the art in parameter estimation for acoustic modeling. Stated as a system identification problem, this first edition focuses on plate reverberation—an archetypal dense, modal and weakly damped acoustic system. Participants tackled two tasks: (A) estimating the physical parameters of a vibrating plate from its impulse response, and (B) recovering the modal parameters of the same system. Both rest on a simulation framework based on the damped Kirchhoff–Love plate equation, and both are posed and scored entirely on synthetic data produced by that framework: no measurement of a real plate is involved. Two participants solved Task A down to machine precision by different strategies: one a neural network trained on a very large dataset, and one gradient-free optimization with many inexpensive evaluations. Task B proved considerably harder: the best submission attains a relative error of 0.33 on a [0, 2] scale, and every method recovers modal frequencies and decay rates far more accurately than modal gains. A complementary frequency-domain evaluation reorders the ranking and exposes a systematic gain bias to which the per-mode metric is blind.
Download Speech/music discrimination based on a new warped LPC-based feature and linear discriminant analysis Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a low complexity but effective approach, which exploits only one simple feature, called Warped LPC-based Spectral Centroid (WLPCSC). Comparison between WLPC-SC and the classical features proposed in [9] is performed, aiming to assess the good discriminatory power of the proposed feature. The length of the vector for describing the proposed psychoacoustic based feature is reduced to a few statistical values (mean, variance and skewness), which are then transformed to a new feature space by applying LDA with the aim of increasing the classification accuracy percentage. The classification task is performed by applying SVM to the features in the transformed space. The classification results for different types of music and speech show the good discriminating power of the proposed approach.
Download Synthesizer Sound Matching Using Audio Spectrogram Transformers Systems for synthesizer sound matching, which automatically set the parameters of a synthesizer to emulate an input sound, have the potential to make the process of synthesizer programming faster and easier for novice and experienced musicians alike, whilst also affording new means of interaction with synthesizers. Considering the enormous variety of synthesizers in the marketplace, and the complexity of many of them, general-purpose sound matching systems that function with minimal knowledge or prior assumptions about the underlying synthesis architecture are particularly desirable. With this in mind, we introduce a synthesizer sound matching model based on the Audio Spectrogram Transformer. We demonstrate the viability of this model by training on a large synthetic dataset of randomly generated samples from the popular Massive synthesizer. We show that this model can reconstruct parameters of samples generated from a set of 16 parameters, highlighting its improved fidelity relative to multi-layer perceptron and convolutional neural network baselines. We also provide audio examples demonstrating the out-of-domain model performance in emulating vocal imitations, and sounds from other synthesizers and musical instruments.
Download Onset-Informed Source Separation Using Non-Negative Matrix Factorization With Binary Masks This paper describes a new onset-informed source separation
method based on non-negative matrix factorization (NMF) with binary masks. Many previous approaches to separate a target instrument sound from polyphonic music have used side-information of
the target that is time-consuming to prepare. The proposed method
leverages the onsets of the target instrument sound to facilitate separation. Onsets are useful information that users can easily generate by tapping while listening to the target in music. To utilize
onsets in NMF-based sound source separation, we introduce binary masks that represent on/off states of the target sound. Binary
masks are formulated as Markov chains based on continuity of musical instrument sound. Owing to the binary masks, onsets can be
handled as a time frame in which the binary masks change from
off to on state. The proposed model is inferred by Gibbs sampling, in which the target sound source can be sampled efficiently
by using its onsets. We conducted experiments to separate the target melody instrument from recorded polyphonic music. Separation results showed about 2 to 10 dB improvement in target source
to residual noise ratio compared to the polyphonic sound. When
some onsets were missed or deviated, the method is still effective
for target sound source separation.
Download Bio-Inspired Optimization of Parametric Onset Detectors 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.