Download Extraction of Metrical Structure from Music Recordings Rhythm is a fundamental aspect of music and metrical structure is an important rhythm-related element. Several mid-level features encoding metrical structure information have been proposed in the literature, although the explicit extraction of this information is rarely considered. In this paper, we present a method to extract the full metrical structure from music recordings without the need for any prior knowledge. The algorithm is evaluated against expert annotations of metrical structure for the GTZAN dataset, each track being annotated multiple times. Inter-annotator agreement and the resulting upper bound on algorithm performance are evaluated. The proposed system reaches 93% of this upper limit and largely outperforms the baseline method.
Download Advanced Fourier Decomposition for Realistic Drum Synthesis This paper presents a novel method of analysing drum sounds,
demonstrating that this can form the basis of a highly realistic synthesis technique for real-time use. The synthesis method can be
viewed as an extension of IFFT synthesis; here we exploit the fact
that audio signals can be recovered from solely the real component of their discrete Fourier transform (RDFT). All characteristics
of an entire drum sample can therefore be conveniently encoded
in a single, real-valued, frequency domain signal. These signals
are interpreted, incorporating the physics of the instrument, and
modelled to investigate how the perceptual features are encoded.
The model was able to synthesize drum sound components in such
detail that they could not be distinguished in an ABX test. This
method may therefore be capable of outperforming existing synthesis techniques, in terms of realism.
Sound examples available here.
Download Towards Efficient Modelling of String Dynamics: A Comparison of State Space and Koopman Based Deep Learning Methods This paper presents an examination of State Space Models (SSM) and Koopman-based deep learning methods for modelling the dynamics of both linear and non-linear stiff strings. Through experiments with datasets generated under different initial conditions and sample rates, we assess the capacity of these models to accurately model the complex behaviours observed in string dynamics. Our findings indicate that our proposed Koopman-based model performs as well as or better than other existing approaches in nonlinear cases for long-sequence modelling. We inform the design of these architectures with the structure of the problems at hand. Although challenges remain in extending model predictions beyond the training horizon (i.e., extrapolation), the focus of our investigation lies in the models’ ability to generalise across different initial conditions within the training time interval. This research contributes insights into the physical modelling of dynamical systems (in particular those addressing musical acoustics) by offering a comparative overview of these and previous methods and introducing innovative strategies for model improvement. Our results highlight the efficacy of these models in simulating non-linear dynamics and emphasise their wide-ranging applicability in accurately modelling dynamical systems over extended sequences.