Jeroen Koppens

Delivering Scalable Audio Experiences using AC-4

AC-4 is a state-of-the-art audio codec standardized in ETSI (TS 103 190 and TS 103 190-2) and included in the DVB toolbox (TS 101 154 V2.2.1 and DVB BlueBook A157) and, at the time of writing, is a candidate standard for ATSC 3.0 as per A/342 part 2. AC-4 is an audio codec designed to address the current and future needs of video and audio entertainment services, including broadcast and Internet streaming. As such, it incorporates a number of features beyond the traditional audio coding algorithms, such as capabilities to support immersive and personalized audio, support for advanced loudness management, video-frame synchronous coding, dialog enhancement, etc. This paper will outline the thinking behind the design of the AC-4 codec, explain the different coding tools used, the systemic features included, and give an overview of performance and applications. It further outlines metadata aspects (immersive and personalized, essential for broadcast), metadata carriage, aspects of interchange of immersive programing, as well as immersive playback and rendering.

An efficient masking model for audio coding exploiting spectro‐temporal masking

Perceptual audio coding achieves part of its coding efficiency by spectrally shaping the quantization noise such that it is masked by the audio signal to be encoded. In order to determine how much quantization noise is allowed within each frequency band and time interval a masking model is used to predict a masking curve specifying the maximally allowed quantization noise level within each frequency band. In most audio coders only spectral masking properties of the audio signal are used. The model by Dau et al. [J. Acoust. Soc. Am. 99, Vol. 3615, 1996] provides an interesting approach to also model temporal masking. Since this model operates as an artificial observer it only predicts whether the quantization noise is audible or not in the presence of the audio signal. In order to determine the most efficient quantization noise shape, the encoder needs to iteratively adapt the noise shape and evaluate each option with the model. This implies a highly computational complex encoding algorithm. In this contri...