Directional Audio Coding (DirAC) is a proven method for parametrically representing a 3D audio scene in B-format and is capable of reproducing it on arbitrary loudspeaker layouts. Although such a method seems well suited for low bitrate Ambisonic transmission, little work has been done on the feasibility of building a real system upon it. In this paper, we present a DirAC-based coding for Higher-Order Ambisonics (HOA), developed as part of a standardisation effort to extend the 3GPP EVS codec to immersive communications. Starting from the first-order DirAC model, we show how to reduce algorithmic delay, the bitrate required for the parameters and complexity by bringing the full synthesis in the spherical harmonic domain. The evaluation of the proposed technique for coding 3rdorder Ambisonics at bitrates from 32 to 128 kbps shows the relevance of the parametric approach compared with existing solutions.

High Resolution Envelope Processing (HREP) is a new tool for improved perceptual coding of audio signals that predominantly consist of many dense transient events, such as applause, rain drop sounds, etc. These signals have traditionally been very difficult to code for perceptual audio codecs, particularly at low bit rates. Based on the gain control principle, HREP acts as a pre-/post-processor pair to perceptual audio codecs and preserves the temporal fine structure and subjective quality of applause-like signals. Subjective tests have shown a significant improvement in audio quality of around 12 MUSHRA points by HREP processing at 48 kbps stereo when used together with an MPEG-H 3D Audio codec. The new coding tool has been adopted as part of MPEG-H 3D Audio Second Edition.

In low-bitrate audio coding, modern coders often rely on efficient parametric techniques to enhance the performance of the waveform preserving transform coder core. While the latter features well-known perceptually adapted quantization of spectral coefficients, parametric techniques reconstruct the signal parts that have been quantized to zero by the encoder to meet the low-bitrate constraint. Large numbers of zeroed spectral values and especially consecutive zeros constituting gaps often lead to audible artifacts at the decoder. To avoid such artifacts the new 3GPP Enhanced Voice Services (EVS) coding standard utilizes noise filling and intelligent gap filling (IGF) techniques, guided by spectral envelope information. In this paper the underlying considerations of the parametric energy adjustment and transmission in EVS and its relation to noise filling, IGF, and tonality preservation are presented. It is further shown that complex-valued IGF envelope calculation in the encoder improves the temporal energy stability of some signals while retaining real-valued decoder-side processing.

We investigate the problem of sparse modeling for predictive coding and introduce an efficient algorithm for computing sparse stereo linear predictors for lossless audio compression. Sparse linear predictive coding offers both improved compression and reduction of decoding complexity compared with non-sparse linear predictive coding. The modeling part amounts to finding the optimal structure of a sparse linear predictor using a fully implementable minimum description length (MDL) approach. The MDL criterion, simplified conveniently under realistic assumptions, is approximately minimized by a greedy algorithm which solves sequentially least squares partial problems, where theLDLTfactorization ensures numerically stable solutions and facilitates a quasi-optimal quantization of the parameter vector. The overall compression system built around this modeling tool is shown to achieve the main goals: improved compression and, even more importantly, faster decoding speeds than the state of the art lossless audio compression methods. The optimal MDL sparse predictors are shown to provide parametric spectra that constitute new alternative spectral descriptors, capturing important regularities missed by the optimal MDL non-sparse predictors.

We propose a context conditioning scheme for encoding the prediction residuals when compressing files containing companded signals. Our scheme encompasses decompanding of the signals, performing linear prediction in the decompanded domain, and then companding back the predicted value into a companded prediction (CP) value, which will differ from the true companded value by an amount called companded prediction residual (CPR). The proposed context conditioning scheme for encoding the CPR, uses a probability distribution conditional on a context made up of two quantities: (1) the predicted value and (2) a scale parameter of the background probability distribution function assumed for the decompanded domain residuals. Various context building schemes and various storing strategies can be used to obtain the necessary conditional coding distribution of the CPR, to be used with an arithmetic coder or range coder. The implementation in fixed point precision can be done very efficiently and with very low memory requirements.

In this paper we study the problem of optimally quantizing the least square estimates and we introduce a method where the quantized estimate vector is obtained by a sequence of interleaved optimizationquantization scalar stages. We show how the general approach can be reduced to a simple and efficient algorithm when connecting it to the LDLTsolver for general LS problems. The application to quantization of the linear prediction coefficients for audio lossless coding reveals the high performance of the approach, leading to topmost performance in the class of frame based audio coders, surpassing significantly the performance of the current MPEG4-ALS standard.

This paper introduces a novel prediction structure for improving the lossless compression ratio, by accounting for companding nonlinearities of different sample-based audio formats. This applies to a wide class of formats including a-law, μ-law, DAT-LP (digital audio tape recorders), DV-LP (digital video camcorders), and HDCD (high definition compatible digital). The proposed prediction structure obtains significant compression improvements (8-12%) over traditional linear prediction for a-law, μ-law, DAT-LP, DV-LP and also small compression improvements for HDCD. The improvement in compression can also be used for the detection of nonlinearities in HDCD format, making possible to play HDCD CDs at improved audio resolution in ordinary public domain players.

Summary form only given. We propose a new paradigm for lossless audio compression, where prediction and residual coding are combined in a single logical stage, taking advantage of the fine statistical structure of the original signal. We obtained significant compression gains for integer signals which were normalized, companded, or recorded using DAT-LP. We compared our existing lossless audio compressor OptimFROG and the new modified variant on a large corpus of 73 Audio CDs. The encoding time was increased with 9.6% and the decoding time with 6.1%. Overall, OptimFROG (normal) obtained a compression (compressed size/original size) of 55.96%, the new variant 55.36%, and Monkey's Audio (high) 56.10%. For 19 CDs we obtained compression improvements up to 12.35% and on average 2.14%, for 14 CDs we obtained small compression improvements on average 0.24%, and the remaining CDs did not present improvements. For mu-law decoded, A-law decoded, and DAT-LP audio, we obtained on one file compression improvements of 28.66%, 22.56%, and 20.98%, respectively. The new paradigm can be also applied to 24 bit lossless audio compression and to lossless image compression (further favored by the small bit depth per color)

Summary form only given. In this paper, we propose a novel approach for the second step of the Burrows-Wheeler compression algorithm, based on the idea that the probabilities of events are not continuous valued, but are rather quantized with respect to a specific class of base functions. The first pass of encoding transforms the input sequence x into sequence x/spl tilde/. The second pass models and codes x/spl tilde/using entropy coding. The entropy decoding, modeling, and context updating for decoding x/spl tilde/ are the same as the ones used for encoding. We have proved that the quantized local frequency transform is optimal in the case of binary and ternary alphabet memoryless sources, showing that x and x/spl tilde/ have the same entropy; for larger alphabets, we verified this by simulation.

Audio data in the IEEE float format is used by the professional quality audio editors as the format of choice during the editing process and also for storing the intermediate results. The proposed algorithm transforms the floating-point values into integer values, in a completely portable way across architectures and compilers. The transform maintains all the important properties of the original values, such as magnitude relations, linear temporal relations and, most importantly, compressibility. It produces a sequence of integers and an additional binary stream used for lossless reconstruction of the original values. Furthermore, the proposed algorithm can be successfully applied to other areas such as medical, science and space data compression. Our algorithm obtains on average 7% better compression than the existing multimedia-aware compression algorithms, while being also faster.

Summary form only given. A new generalized stereo decorrelation algorithm and optimal predictor for lossless audio compression is proposed. The proposed algorithm yields around 1.5% better compression than the previous state-of-the-art algorithms. With this approach, the stereo decorrelation and the prediction can be merged into a single step, which can lead to a global minimum for the combined problem. The integer valued audio signal to be encoded is considered. The two discrete time equations, which model the combined problem using an order S intra-channel prediction and an order D inter-channel prediction, are given.