Abstract-Several studies have pointed out the need for accurate mid-level representations of music signals for information retrieval and signal processing purposes. In this paper, we propose a new mid-level representation based on the decomposition of a signal into a small number of sound atoms or molecules bearing explicit musical instrument labels. Each atom is a sum of windowed harmonic sinusoidal partials whose relative amplitudes are specific to one instrument, and each molecule consists of several atoms from the same instrument spanning successive time windows. We design efficient algorithms to extract the most prominent atoms or molecules and investigate several applications of this representation, including polyphonic instrument recognition and music visualization.

We propose in this paper a simple fusion framework for underdetermined audio source separation. This framework can be applied to a wide variety of source separation algorithms providing that they estimate time-frequency masks. Fusion principles have been successfully implemented for classification tasks. Although it is similar to classification, audio source separation does not usually take advantage of such principles. We thus introduce some general fusion rules inspired by classification and we evaluate them in the context of voice extraction. Experimental results are promising as our proposed fusion rule can improve separation results up to 1 dB in SDR.

This paper concerns the adaptation of spectrum dictionaries in audio source separation with supervised learning. Supposing that samples of the audio sources to separate are available, a filter adaptation in the frequency domain is proposed in the context of Non-Negative Matrix Factorization with the Itakura-Saito divergence. The algorithm is able to retrieve the acoustical filter applied to the sources with a good accuracy, and demonstrates significantly higher performances on separation tasks when compared with the non-adaptive model.

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