Mark D. Plumbley

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We present a simple and efficient method for beat tracking of musical audio. With the aim of replicating the human ability of tapping in time to music, we formulate our approach using a two state model. The first state performs tempo induction and tracks tempo changes, while the second maintains contextual continuity within a single tempo hypothesis. Beat(More)
Over the last decade, there has been an increased interest in the speech and audio processing community in code dissemination and public evaluation of proposed methods. Public evaluation can serve as a reference point for the performance of proposed methods and can also be used for studying performance improvements throughout the years. For example, source(More)
We present a system for adaptive spectral basis decomposition that learns to identify independent spectral features given a sequence of short-term Fourier spectra. When applied to recordings of polyphonic piano music, the individual notes are identified as salient features, and hence each short-term spectrum is decomposed into a sum of note spectra; the(More)
The instantaneous noise-free linear mixing model in independent component analysis is largely a solved problem under the usual assumption of independent nongaussian sources and full column rank mixing matrix. However, with some prior information on the sources, like positivity, new analysis and perhaps simplified solution methods may yet become possible. In(More)
We consider the task of solving the independent component analysis (ICA) problem x=As given observations x, with a constraint of nonnegativity of the source random vector s. We refer to this as nonnegative independent component analysis and we consider methods for solving this task. For independent sources with nonzero probability density function (pdf)(More)
Measures such as entropy and mutual information can be used to characterise random processes. In this paper, we propose the use of several time-varying information measures, computed in the context of a probabilistic model which evolves as a sample of the process unfolds, as a way to characterise temporal structure in music. One such measure is a novel(More)
We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to be known. The signal is decomposed into overlapping time-domain frames and the restoration problem is(More)
We introduce a new greedy algorithm to find approximate sparse representations s of x = As by finding the Basis Pursuit (BP) solution of the linear program min{‖s‖ 1 | x = As}. The proposed algorithm is based on the geometry of the polar polytope P ∗ = {c | à c ≤ 1} where à = [A,−A] and searches for the vertex c ∈ P ∗ which maximizes x c using a path(More)
This paper describes a newly-launched public evaluation challenge on acoustic scene classification and detection of sound events within a scene. Systems dealing with such tasks are far from exhibiting human-like performance and robustness. Undermining factors are numerous: the extreme variability of sources of interest possibly interfering, the presence of(More)
In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range(More)