A method for music playlist generation, using assimilated Gaussian mixture models (GMMs) in self organizing maps (SOMs) is presented. Traditionally, the neurons in a SOM are represented by vectors, but in this paper we propose to use GMMs instead. To this end, we introduce a method to adapt a GMM such that its distance to a second GMM decreases at a controllable rate. Self organization is demonstrated using a small music database and a music classification task
This paper describes a statistical music structure analysis method that splits an audio signal of po...
We present a content-based music collection exploration tool based on a variation of the Self-Organi...
Abstract — In this study, we propose a new Self-Organizing Map (SOM) algorithm considering Winning F...
We propose a method for music genre classification based on a Self-Organizing Map (SOM) - type netwo...
International audienceThis text presents some aspects of the Neuro-muse 1 project developed at CIRM....
We present an architecture able to recognise pitches and to internally simulate likely continuations...
Digital storage of personal music collections and cloud-based music services (e.g. Pandora, Spotify)...
Due to an increasing amount of music being made available in digital form in the Internet, an automa...
A Bayesian self-organising map (BSOM) is proposed for learning mixtures of Gaussian distributions. I...
Music is a link between cognition and emotion, andpeople are not able to share same feeling for a so...
We present a study of a novel variant of the Self-Organizing Map (SOM) called the Associative Self-O...
The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning techn...
The paper describes a hierarchical Markov modeling strategy that offers the advantages of a statisti...
Most real life applications have huge search spaces. Evolutionary Computation provides an advantage ...
With so much modern music being so widely available both in electronic form and in more traditional ...
This paper describes a statistical music structure analysis method that splits an audio signal of po...
We present a content-based music collection exploration tool based on a variation of the Self-Organi...
Abstract — In this study, we propose a new Self-Organizing Map (SOM) algorithm considering Winning F...
We propose a method for music genre classification based on a Self-Organizing Map (SOM) - type netwo...
International audienceThis text presents some aspects of the Neuro-muse 1 project developed at CIRM....
We present an architecture able to recognise pitches and to internally simulate likely continuations...
Digital storage of personal music collections and cloud-based music services (e.g. Pandora, Spotify)...
Due to an increasing amount of music being made available in digital form in the Internet, an automa...
A Bayesian self-organising map (BSOM) is proposed for learning mixtures of Gaussian distributions. I...
Music is a link between cognition and emotion, andpeople are not able to share same feeling for a so...
We present a study of a novel variant of the Self-Organizing Map (SOM) called the Associative Self-O...
The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning techn...
The paper describes a hierarchical Markov modeling strategy that offers the advantages of a statisti...
Most real life applications have huge search spaces. Evolutionary Computation provides an advantage ...
With so much modern music being so widely available both in electronic form and in more traditional ...
This paper describes a statistical music structure analysis method that splits an audio signal of po...
We present a content-based music collection exploration tool based on a variation of the Self-Organi...
Abstract — In this study, we propose a new Self-Organizing Map (SOM) algorithm considering Winning F...