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This is a presentation of the paper (http://www.aes.org/e-lib/browse.cfm?elib=21902), presented at the 153rd Audio Engineering Society Convention (https://sched.co/1CK3O).

Objective audio quality assessment systems often use perceptual models to predict the subjective quality scores of processed signals, as reported in listening tests. Most systems map different metrics of perceived degradation into a single quality score predicting subjective quality. This requires a quality mapping stage that is informed by real listening test data using statistical learning (\iec a data-driven approach) with distortion metrics as input features.

This is a presentation of the paper (http://www.aes.org/e-lib/browse.cfm?elib=21478), presented virtually at the 151st Audio Engineering Society Convention (https://sched.co/mIhg).

It was recently shown that the combination of source prediction, two-times oversampling, and noise shaping, can be used to obtain a robust (multiple-description) audio coding frame- work for networks with packet loss probabilities less than 10%. Specifically, it was shown that audio signals could be encoded into two descriptions (packets), which were separately sent over a communication channel. Each description yields a desired performance by itself, and when they are combined, the performance is improved.

This is a presentation of the paper (http://www.aes.org/e-lib/browse.cfm?elib=20769), presented virtually at the 148th Audio Engineering Society Convention (https://www.eventscribe.com/2020/VirtualVienna/).

These are the slides from the video presentation at ICASSP 2020 of the paper "Source Coding of Audio Signals with a Generative Model".

We consider source coding of audio signals with the help of a generative model. We use a construction where a waveform is first quantized, yielding a finite bitrate representation. The waveform is then reconstructed by random sampling from a model conditioned on the quantized waveform. The proposed coding scheme is theoretically analyzed. Using SampleRNN as the generative model, we demonstrate that the proposed coding structure provides performance competitive with state-of-the-art source coding tools for specific categories of audio signals.

In this paper, the improvement of the cascaded prediction method was presented. The prediction method with backward adaptation and extended Ordinary Least Square (OLS+) was presented. An own approach to implementation of the effective context-dependent constant component removal block was used. Also the improved adaptive arithmetic coder with short, medium and long-term adaptation was used and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency.

This is a poster presented Thursday August 22, 2019 at the International Symposium on Auditory and Audiological Research (ISAAR). https://www.isaar.eu/index.php

SP.72 - Learning about perception of temporal fine structure by building audio codecs

https://whova.com/embedded/session/isaar_201908/701162/