12 research outputs found
Deep Sound Field Reconstruction in Real Rooms:Introducing the ISOBEL Sound Field Dataset
Knowledge of loudspeaker responses are useful in a number of applications,
where a sound system is located inside a room that alters the listening
experience depending on position within the room. Acquisition of sound fields
for sound sources located in reverberant rooms can be achieved through labor
intensive measurements of impulse response functions covering the room, or
alternatively by means of reconstruction methods which can potentially require
significantly fewer measurements. This paper extends evaluations of sound field
reconstruction at low frequencies by introducing a dataset with measurements
from four real rooms. The ISOBEL Sound Field dataset is publicly available, and
aims to bridge the gap between synthetic and real-world sound fields in
rectangular rooms. Moreover, the paper advances on a recent deep learning-based
method for sound field reconstruction using a very low number of microphones,
and proposes an approach for modeling both magnitude and phase response in a
U-Net-like neural network architecture. The complex-valued sound field
reconstruction demonstrates that the estimated room transfer functions are of
high enough accuracy to allow for personalized sound zones with contrast ratios
comparable to ideal room transfer functions using 15 microphones below 150 Hz
Deep Joint Embeddings of Context and Content for Recommendation
This paper proposes a deep learning-based method for learning joint
context-content embeddings (JCCE) with a view to context-aware recommendations,
and demonstrate its application in the television domain. JCCE builds on recent
progress within latent representations for recommendation and deep metric
learning. The model effectively groups viewing situations and associated
consumed content, based on supervision from 2.7 million viewing events.
Experiments confirm the recommendation ability of JCCE, achieving improvements
when compared to state-of-the-art methods. Furthermore, the approach shows
meaningful structures in the learned representations that can be used to gain
valuable insights of underlying factors in the relationship between contextual
settings and content properties.Comment: Accepted for CARS 2.0 - Context-Aware Recommender Systems Workshop @
RecSys'1