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Found 6 papers, 0 papers with code
Geometrically-Motivated Primary-Ambient Decomposition With Center-Channel Extraction
A geometrically-motivated method for primary-ambient decomposition is proposed and evaluated in an up-mixing application.
Sampling Frequency Independent Dialogue Separation
The models are trained with audio sampled at 8 kHz.
Dialog+ in Broadcasting: First Field Tests Using Deep-Learning-Based Dialogue Enhancement
To address this, Fraunhofer IIS has developed a deep-learning solution called Dialog+, capable of enabling speech level personalization also for content with only the final audio tracks available.
Controlling the Perceived Sound Quality for Dialogue Enhancement with Deep Learning
Speech enhancement attenuates interfering sounds in speech signals but may introduce artifacts that perceivably deteriorate the output signal.
Controlling the Remixing of Separated Dialogue with a Non-Intrusive Quality Estimate
The 2f-model requires the reference target source as an input, but this is not available in many applications.
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation
The music separation models are selected as they share the number of channels (2) and sampling rate (44. 1 kHz or higher) with the considered broadcast content, and vocals separation in music is considered as a parallel for dialog separation in the target application domain.
