Dear Subscribers of the Colloquium Newsletter,
please note that the lecture (see below) will take place on Friday, October 20, 2023, *at 10:45 a.m.*
kind regards Simone Sedgwick
-------- Weitergeleitete Nachricht -------- Betreff: Communication Technology Colloquium at IKS Datum: Wed, 11 Oct 2023 13:32:59 +0200 Von: Simone Sedgwick sedgwick@iks.rwth-aachen.de An: kommunikationstechnik-kolloquium@lists.rwth-aachen.de
Dear subscribers of the colloquium newsletter,
we are happy to inform you about the next date of our Communication Technology Colloquium.
*Friday, October 20, 2023* *Speaker*: Elgiz Coskun *Time:* 11:00 a.m. *Location*: hybrid - Lecture room 4G and https://rwth.zoom.us/j/61215027648?pwd=MTJvayt5bkdka04raWZVempPZGE0Zz09
Meeting-ID: 612 1502 7648 Passwort: 380386
*Master-Lecture: **
Optimization of an Instrumental Audio Quality Assessment Approach Using Machine Learning Methods*
The assessment of audio system playback quality involves diverse methods, including auditory tests, technical parameter measurements, and instrumental evaluation techniques. The instrumental methods emulate human auditory perception using algorithmic steps, transforming analysis results into perceptual scales. Recent advancements include applying Machine Learning (ML) and Deep Learning (DL) to instrumental assessment, enhancing prediction quality and operational efficiency. This study proposes a double-ended model for audio quality prediction, aiming to match the prediction quality of an existing method, called Multi-Dimensional Audio Quality Score (MDAQS), while improving efficiency. A Deep Neural Network (DNN) model is designed, utilizing a Convolutional Neural Network (CNN)-Encoder for feature extraction, Self-Attention for time-weighting, and specialized attention-pooling. Data is gathered from binaural measurements using various audio systems and augmented to enhance model resilience. Preprocessing includes labeling and domain transformation using a sophisticated hearing model. The model is trained with preprocessed labeled data, and its prediction results are compared to the target scores obtained via MDAQS. Next, the pretrained model is extended to predict quality dimensions directly comparable to auditory results in listening tests. Parameters of the pretrained model are kept fixed during the second training phase due to limited auditory data. Predictions are evaluated using metrics accounting for auditory result uncertainty.
All interested parties are cordially invited, registration is not required.
General information on the colloquium, as well as a current list of dates of the Communication Technology Colloquium can be found at: https://www.iks.rwth-aachen.de/aktuelles/kolloquium
Simone Sedgwick Secretariat Institute of Communication Systems(IKS) Prof. Dr.-Ing. Peter Jax RWTH Aachen University Muffeter Weg 3a, 52074 Aachen, Germany +49 241 80 26956(phone) +49 241 80 22254(fax) sedgwick@iks.rwth-aachen.de https://www.iks.rwth-aachen.de/