Timbral modeling is fundamental in content based music similarity systems. It is usually achieved by modeling the short term features by a Gaussian Model (GM) or Gaussian Mixture Models (GMM). In this article we propose to achieve this goal by using the GMM-supervector approach. This method allows to represent complex statistical models by an Euclidean vector. Experiments performed for the music similarity task showed that this model outperform state of the art approches. Moreover, it reduces the similarity search time by a factor of ≈ 100 compared to state of the art GM modeling. Furthermore, we propose a new supervector normalization which makes the GMM-supervector approach more preformant for the music similarity task. The proposed normalization can be applied to other Euclidean models.

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In this paper we address the problem of the description of music production techniques from the audio signal. Over the past decades sound engineering techniques have changed drastically. New recording technologies, extensive use of compressors and limiters or new stereo techniques have deeply modified the sound of records. We propose three features to describe these evolutions in music production. They are based on the dynamic range of the signal, energy difference between channels and phase spread between channels. We measure the relevance of these features on a task of automatic classification of Pop/Rock songs into decades. In the context of Music Information Retrieval this kind of description could be very useful to better describe the content of a song or to assess the similarity between songs.

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