3 research outputs found
Adaptive Scattering Transforms for Playing Technique Recognition
Playing techniques contain distinctive information about musical expressivity and interpretation. Yet, current research in music signal analysis suffers from a scarcity of computational models for playing techniques, especially in the context of live performance. To address this problem, our paper develops a general framework for playing technique recognition. We propose the adaptive scattering transform, which refers to any scattering transform that includes a stage of data-driven dimensionality reduction over at least one of its wavelet variables, for representing playing techniques. Two adaptive scattering features are presented: frequency-adaptive scattering and direction-adaptive scattering. We analyse seven playing techniques: vibrato, tremolo, trill, flutter-tongue, acciaccatura, portamento, and glissando. To evaluate the proposed methodology, we create a new dataset containing full-length Chinese bamboo flute performances (CBFdataset) with expert playing technique annotations. Once trained on the proposed scattering representations, a support vector classifier achieves state-of-the-art results. We provide explanatory visualisations of scattering coefficients for each technique and verify the system over three additional datasets with various instrumental and vocal techniques: VPset, SOL, and VocalSet
Scale Dependencies and Self-Similarity Through Wavelet Scattering Covariance
We introduce a scattering covariance matrix which provides non-Gaussian
models of time-series having stationary increments. A complex wavelet transform
computes signal variations at each scale. Dependencies across scales are
captured by the joint covariance across time and scales of complex wavelet
coefficients and their modulus. This covariance is nearly diagonalized by a
second wavelet transform, which defines the scattering covariance. We show that
this set of moments characterizes a wide range of non-Gaussian properties of
multi-scale processes. This is analyzed for a variety of processes, including
fractional Brownian motions, Poisson, multifractal random walks and Hawkes
processes. We prove that self-similar processes have a scattering covariance
matrix which is scale invariant. This property can be estimated numerically and
defines a class of wide-sense self-similar processes. We build maximum entropy
models conditioned by scattering covariance coefficients, and generate new
time-series with a microcanonical sampling algorithm. Applications are shown
for highly non-Gaussian financial and turbulence time-series
Maximum-entropy Scattering Models for Financial Time Series
International audienceModeling time series with complex statistical properties such as heavy-tails, long-range dependence, and temporal asymmetries remains an open problem. In particular, financial time series exhibit such properties. Existing models suffer from serious limitations and often rely on high-order moments. We introduce a wavelet-based maximum entropy model for such random processes, based on new scattering and phase-harmonic moments. We analyze the model's performance with a synthetic multifractal random process and real-world financial time series. We show that scattering moments capture heavy tails and multifractal properties without estimating high-order moments. Further, we show that additional phase-harmonic terms capture temporal asymmetries