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The CHiME challenge series aims to advance far field speech recognition technology by promoting research at the interface of signal processing and automatic speech recognition. This paper presents the design and outcomes of the 3rd CHiME Challenge, which targets the performance of automatic speech recognition in a real-world, commercially-motivated(More)
We introduce a generic model of emergence of musical categories during the listening process. The model is based on a preprocessing and a categorization module. Preprocessing results in a perceptually plausible representation of music events extracted from audio or symbolic input. The categorization module lets a taxonomy of musical entities emerge(More)
A causal system to represent a stream of music into musical events, and to generate further expected events, is presented. Starting from an auditory front-end which extracts low-level (i.e. MFCC) and mid-level features such as onsets and beats, an unsupervised clustering process builds and maintains a set of symbols aimed at representing musical stream(More)
This research focuses on the removal of the singing voice in polyphonic audio recordings under real-time constraints. It is based on time-frequency binary masks resulting from the combination of azimuth, phase difference and absolute frequency spectral bin classification and harmonic-derived masks. For the harmonic-derived masks, a pitch likelihood(More)
We present a method for lead instrument separation using an available musical score that may not be properly aligned with the polyphonic audio mixture. Improper alignment degrades the performance of existing score-informed source separation algorithms. Several techniques are proposed to manage local and global misalignments, such as a score information(More)
In unsupervised learning, such as clustering, the problem occurs how to evaluate the results. In particular, neither the number of clusters nor the mapping between eventually known reference classes, e.g. generated from annotations, and the clusters are known. In this report, a method is suggested that adapts the F-measure for supervised classification to(More)