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*Dienstag, 23. April 2019* *Vortragender*: Si Hu *Ort:* Hörsaal 4G IKS *Zeit*: 14:00 Uhr

*Master-Vortrag*: Investigations on Joint Application of Blind Source Separation and Primary-Ambient Extraction to Stereo Signals

Due to the rapid development of surround sound systems, audio files in multi-channel format are increasingly required. In consideration of the fact that most legacy audio recordings are in stereo format, it is favorable to convert these audio recordings in stereo multi-channel format which refers to upmixing technology. In the upmixing process, so-called primary and ambient components play an important role. They are extracted from the input stereo signal by means of Primary-Ambient Extraction (PAE). There are many PAE approaches available, but they have a common shortcoming. When they handle audio signals with multiple concurrent primary sources, the extraction quality will dastically degrade and the listening experience will be significantly impaired.

In order to address this issue, we consider to incorporate Blind Source Separation (BSS) into the PAE process. In BSS, it is investigated how to recover every single sound source form a mixed audio signal. The basic idea is that an audio signal with multiple concurrent primary sources can be separated into several sub-signals by means of BSS. Each sub-signal is expected to contain only on primary source. Then PAE is executed for every single sub-signal. Theoretically, PAE processing should be facilitated, because PAE works well when dealing with the signals with only a single primary soure.

In this thesis, the feasibility of this idea is validated. Non-negative Tensor Factorization (NTF), as a prevalent BSS technique, is employed in the experiments. We propose several NTF based strategies to execute PAE on audio input signals with three primary sources. A series of numerical experimental results and subjective listening tests show that the PAE performance can be enhanced in this way in the case of multiple primary sources.

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