This thesis is related to the field of Sound Source Separation (SSS). It addresses the development and evaluation of these techniques for their application in the resynthesis of high-realism sound scenes by means of Wave Field Synthesis (WFS). Because the vast majority of audio recordings are preserved in twochannel stereo format, special up-converters are required to use advanced spatial audio reproduction formats, such as WFS. This is due to the fact that WFS needs the original source signals to be available, in order to accurately synthesize the acoustic field inside an extended listening area. Thus, an object-based mixing is required. Source separation problems in digital signal processing are those in which several signals have been mixed together and the objective is to find out what the original signals were. Therefore, SSS algorithms can be applied to existing two-channel mixtures to extract the different objects that compose the stereo scene. Unfortunately, most stereo mixtures are underdetermined, i.e., there are more sound sources than audio channels. This condition makes the SSS problem especially difficult and stronger assumptions have to be taken, often related to the sparsity of the sources under some signal transformation. This thesis is focused on the application of SSS techniques to the spatial sound reproduction field. As a result, its contributions can be categorized within these two areas. First, two underdetermined SSS methods are proposed to deal efficiently with the separation of stereo sound mixtures. These techniques are based on a multi-level thresholding segmentation approach, which enables to perform a fast and unsupervised separation of sound sources in the time-frequency domain. Although both techniques rely on the same clustering type, the features considered by each of them are related to different localization cues that enable to perform separation of either instantaneous or real mixtures.Additionally, two post-processing techniques aimed at improving the isolation of the separated sources are proposed. The performance achieved by several SSS methods in the resynthesis of WFS sound scenes is afterwards evaluated by means of listening tests, paying special attention to the change observed in the perceived spatial attributes. Although the estimated sources are distorted versions of the original ones, the masking effects involved in their spatial remixing make artifacts less perceptible, which improves the overall assessed quality. Finally, some novel developments related to the application of time-frequency processing to source localization and enhanced sound reproduction are presented. / Cobos Serrano, M. (2009). Application of sound source separation methods to advanced spatial audio systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8969 / Palancia

Dans les applications d'écoute active, il est primordial de pouvoir interagir avec les sources individuelles présentes dans le mix, par exemple en changeant leur position spatiale. Dans cette thèse, nous avons proposé des techniques binaurales pour la localisation et la spatialisation, basées sur les différences interaurales en amplitude et en temps d'arrivée. Les techniques sont développées dans le plan temps-fréquence. Elles permettent de localiser et de projeter toute source dans l'espace environnant un auditeur. aussi nous avons mis au point des techniques de séparation binaurale de source basées sur le Maximum de vraisemblance et de masques spatiaux probabilistes. Enfin nous avons étendu les techniques binaurales à des techniques multi-diffusion utilisant un ensemble de haut-parleurs. Les techniques proposées sont éprouvées et comparées à des techniques de référence de la littérature. Pour des performances similaires aux techniques existantes, nos propositions ont un avantage significatif en terme de complexité qui les rendent appropriées aux applications temps-réel. / In active listening applications, it is important to be able to interact with individual sources present in the mix, for example by changing their spatial position. In this thesis, we proposed techniques for binaural localization and spatialization, based on interaural differences in amplitude and in time of arrival. The techniques are developed in the time-frequency plane. They can locate and project sources in the space surrounding a listener. We also developed binaural source separation methods based on the Maximum Likelihood and on spatial probabilistic masks. Finally, we extended binaural spatialization techniques to multi-diffusion techniques which use a set of speakers for diffusion. The proposed techniques are tested and compared to referenced, well-known techniques. For similar performance with the existing ones, our proposed techniques highlight complexity advantages and are suitable for real-time applications.