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.

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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.

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