Download HD-AD: A New Approach to Audio Atomic Decomposition with Hyperdimensional Computing In this paper, we approach the problem of atomic decomposition of audio at the symbolic level of atom parameters through the lens of hyperdimensional computing (HDC) – a non-traditional computing paradigm. Existing atomic decomposition algorithms often operate using waveforms from a redundant dictionary of atoms causing them to become increasingly memory/computationally intensive as the signal length grows and/or the atoms become more complicated. We systematically build an atom encoding using vector function architecture (VFA), a field of HDC. We train a neural network encoder on synthetic audio signals to generate these encodings and observe that the network can generalize to real recordings. This system, we call Hyperdimensional Atomic Decomposition (HD-AD), avoids time-domain correlations all together. Because HD-AD scales with the sparsity of the signal, rather than its length in time, atomic decompositions are often produced much faster than real-time.
Download Hybrid Audio Inpainting Approach with Structured Sparse Decomposition and Sinusoidal Modeling This research presents a novel hybrid audio inpainting approach that considers the diversity of signals and enhances the reconstruction quality. Existing inpainting approaches have limitations, such as energy drop and poor reconstruction quality for non-stationary signals. Based on the fact that an audio signal can be considered as a mixture of three components: tonal, transients, and noise, the proposed approach divides the left and right reliable neighborhoods around the gap into these components using a structured sparse decomposition technique. The gap is reconstructed by extrapolating parameters estimated from the reliable neighborhoods of each component. Component-targeted methods are refined and employed to extrapolate the parameters based on their own acoustic characteristics. Experiments were conducted to evaluate the performance of the hybrid approach and compare it with other stateof-the-art inpainting approaches. The results show the hybrid approach achieves high-quality reconstruction and low computational complexity across various gap lengths and signal types, particularly for longer gaps and non-stationary signals.
Download Sound Morphing by Audio Descriptors and Parameter Interpolation We present a strategy for static morphing that relies on the sophisticated interpolation of the parameters of the signal model and the independent control of high-level audio features. The source and target signals are decomposed into deterministic, quasi-deterministic and stochastic parts, and are processed separately according to sinusoidal modeling and spectral envelope estimation. We gain further intuitive control over the morphing process by altering the interpolated spectrum according to target values of audio descriptors through an optimization process. The proposed approach leads to convincing morphing results in the case of sustained or percussive, harmonic and inharmonic sounds of possibly different durations.
Download Sparse Atomic Modeling of Audio: a Review Research into sparse atomic models has recently intensified in the image and audio processing communities. While other reviews exist, we believe this paper provides a good starting point for the uninitiated reader as it concisely summarizes the state-of-the-art, and presents most of the major topics in an accessible manner. We discuss several approaches to the sparse approximation problem including various greedy algorithms, iteratively re-weighted least squares, iterative shrinkage, and Bayesian methods. We provide pseudo-code for several of the algorithms, and have released software which includes fast dictionaries and reference implementations for many of the algorithms. We discuss the relevance of the different approaches for audio applications, and include numerical comparisons. We also illustrate several audio applications of sparse atomic modeling.
Download Onset Time Estimation for the Analysis of Percussive Sounds using Exponentially Damped Sinusoids Exponentially damped sinusoids (EDS) model-based analysis of sound signals often requires a precise estimation of initial amplitudes and phases of the components found in the sound, on top of a good estimation of their frequencies and damping. This can be of the utmost importance in many applications such as high-quality re-synthesis or identification of structural properties of sound generators (e.g. a physical coupling of vibrating devices). Therefore, in those specific applications, an accurate estimation of the onset time is required. In this paper we present a two-step onset time estimation procedure designed for that purpose. It consists of a “rough" estimation using an STFT-based method followed by a time-domain method to “refine" the previous results. Tests carried out on synthetic signals show that it is possible to estimate onset times with errors as small as 0.2ms. These tests also confirm that operating first in the frequency domain and then in the time domain allows to reach a better resolution vs. speed compromise than using only one frequency-based or one time-based onset detection method. Finally, experiments on real sounds (plucked strings and actual percussions) illustrate how well this method performs in more realistic situations.
Download Damped Chirp Mixture Estimation via Nonlinear Bayesian Regression Estimating mixtures of damped chirp sinusoids in noise is a
problem that affects audio analysis, coding, and synthesis applications. Phase-based non-stationary parameter estimators assume
that sinusoids can be resolved in the Fourier transform domain,
whereas high-resolution methods estimate superimposed components with accuracy close to the theoretical limits, but only for
sinusoids with constant frequencies. We present a new method
for estimating the parameters of superimposed damped chirps that
has an accuracy competitive with existing non-stationary estimators but also has a high-resolution like subspace techniques. After providing the analytical expression for a Gaussian-windowed
damped chirp signal’s Fourier transform, we propose an efficient
variational EM algorithm for nonlinear Bayesian regression that
jointly estimates the amplitudes, phases, frequencies, chirp rates,
and decay rates of multiple non-stationary components that may be
obfuscated under the same local maximum in the frequency spectrum. Quantitative results show that the new method not only has
an estimation accuracy that is close to the Cramér-Rao bound, but
also a high resolution that outperforms the state-of-the-art.