Music Learning with Long Short Term Memory Networks
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We present a data-driven approach to weighting the temporal context of signal energy to be used in a simple speech/non-speech detector (SND). The optimal weights are obtained using linear discriminant analysis (LDA). Regularization is performed to handle n ...
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of di ...
This thesis proposes to analyse symbolic musical data under a statistical viewpoint, using state-of-the-art machine learning techniques. Our main argument is to show that it is possible to design generative models that are able to predict and to generate m ...
Sensory memory is the first step of a complex memorization process. Sensory information is buffered in a "sensory store" for a short period. Then part of it is transferred to working memory, where information can be actively processed and, eventually, thro ...
We present a biologically-inspired neural model addressing the problem of transformations across frames of reference in a posture imitation task. Our modeling is based on the hypothesis that imitation is mediated by two concurrent transformations selective ...
In this article, we introduce a novel approach for monaural source separation with the specific aim to separate a polyphonic musical recording into two main sources: a main instrument (or melody) track and an accompaniment track. To that aim, we propose to ...
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of di ...
In this article we review several successful extensions to the standard Hidden-Markov-Model/Artificial Neural Network (HMM/ANN) hybrid, which have recently made important contributions to the field of noise robust automatic speech recognition. The first ex ...
This thesis proposes to analyse symbolic musical data under a statistical viewpoint, using state-of-the-art machine learning techniques. Our main argument is to show that it is possible to design generative models that are able to predict and to generate m ...
The storage and short-term memory capacities of recurrent neural networks of spiking neurons are investigated. We demonstrate that it is possible to process online many superimposed streams of input. This is despite the fact that the stored information is ...
