Download REDS: A New Asymmetric Atom for Sparse Audio Decomposition and Sound Synthesis In this paper, we introduce a function designed specifically for sparse audio representations. A progression in the selection of dictionary elements (atoms) to sparsely represent audio has occurred: starting with symmetric atoms, then to damped sinusoid and hybrid atoms, and finally to the re-appropriation of the gammatone (GT) and formantwave-function (FOF) into atoms. These asymmetric atoms have already shown promise in sparse decomposition applications, where they prove to be highly correlated with natural sounds and musical audio, but since neither was originally designed for this application their utility remains limited. An in-depth comparison of each existing function was conducted based on application specific criteria. A directed design process was completed to create a new atom, the ramped exponentially damped sinusoid (REDS), that satisfies all desired properties: the REDS can adapt to a wide range of audio signal features and has good mathematical properties that enable efficient sparse decompositions and synthesis. Moreover, the REDS is proven to be approximately equal to the previous functions under some common conditions.
Download Fast Partial Tracking of Audio with Real-Time Capability through Linear Programming This paper proposes a new partial tracking method, based on linear programming, that can run in real-time, is simple to implement, and performs well in difficult tracking situations by considering spurious peaks, crossing partials, and a non-stationary shortterm sinusoidal model. Complex constant parameters of a generalized short-term signal model are explicitly estimated to inform peak matching decisions. Peak matching is formulated as a variation of the linear assignment problem. Combinatorially optimal peak-to-peak assignments are found in polynomial time using the Hungarian algorithm. Results show that the proposed method creates high-quality representations of monophonic and polyphonic sounds.
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.