73 citations · 113 across the 13 of their papers we have counts for
13 papers
Predicting Preferred Dialogue-to-Background Loudness Difference in Dialogue-Separated Audio
Luca Resti, Martin Strauss, Matteo Torcoli +2
Dialogue Enhancement (DE) enables the rebalancing of dialogue and background sounds to fit personal preferences and needs in the context of broadcast audio. When individual audio s…
Better Together: Dialogue Separation and Voice Activity Detection for Audio Personalization in TV
Matteo Torcoli, Emanuël A. P. Habets
In TV services, dialogue level personalization is key to meeting user preferences and needs. When dialogue and background sounds are not separately available from the production st…
Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation
Martin Strauss, Matteo Torcoli, Bernd Edler
Deep generative models for Speech Enhancement (SE) received increasing attention in recent years. The most prominent example are Generative Adversarial Networks (GANs), while norma…
Dialogue Enhancement and Listening Effort in Broadcast Audio: A Multimodal Evaluation
Matteo Torcoli, Thomas Robotham, Emanuël A. P. Habets
Dialogue enhancement (DE) plays a vital role in broadcasting, enabling the personalization of the relative level between foreground speech and background music and effects. DE has…
Geometrically-Motivated Primary-Ambient Decomposition With Center-Channel Extraction
Jouni Paulus, Matteo Torcoli
A geometrically-motivated method for primary-ambient decomposition is proposed and evaluated in an up-mixing application. The method consists of two steps, accommodating a particul…
Sampling Frequency Independent Dialogue Separation
Jouni Paulus, Matteo Torcoli
In some DNNs for audio source separation, the relevant model parameters are independent of the sampling frequency of the audio used for training. Considering the application of dia…