The challenge of coding delay is becoming increasingly recognized as a barrier to the broader implementation of Higher Order Ambisonics (HOA), which is a highly flexible format for spatial audio encoding and reproduction. A low latency is essential for various applications, such as virtual reality, interactive gaming, and live online music sessions. Unfortunately, significant strides in the development of spatial audio codecs often compromise on latency to enhance compression efficiency. This work presents a low-delay codec designed for the compression of HOA signals. The codec uses a combination of singular value decomposition, short-term linear prediction, cascaded long-term prediction, and sub-band coding, as well as entropy coding, in order to maximally compress HOA signals while maintaining a low delay. A variety of configurations allow for algorithmic delays between 54 samples to 206 samples, at the cost of bitrate given a fixed quality level. The proposed codec outperforms the low-delay implementation of a standard codec for HOA lossy compression, both in terms of delay and bitrate at medium and higher quality levels.

This paper proposes an effective universal (on-the-Hy’’ mechanism for stochastic codebook generation in lossy coding of Markov sources. Earlier work has shown that the ratedistortion bound can be asymptotically achieved by a “natural type selection” (NTS) mechanism that iteratively considers asymptotically long source strings (from an unknown distribution P) and regenerates the codebook from a distribution obtained within a maximum likelihood distribution estimation framework, based on observation of a set of K codewords that “d-match’’ (i.e., satisfy the distortion constraint for) a respective set of K independently generated source words. This result was later generalized, in a straightforward manner, to account for source memory, by considering the source as a vector source, i.e., a sequence of super-symbols from a corresponding super-alphabet. While ensuring asymptotic optimality, this extension suffered from a significant practical flaw: it requires asymptotically long vectors or super-symbols, hence exponentially large super-alphabet, in order to approach the rate-distortion bound, even for finite memory sources, e.g., Markov sources. Such exponentially large super-alphabet implies that even a single NTS iteration is intractable, thus compromising the promise of NTS to approach the rate-distortion function, in practice, for sources with memory. This work describes a considerably more efficient and tractable mechanism to achieve asymptotically optimal performance given a prescribed memory constraint, within a practical framework tailored to Markov sources. Specifically, the algorithm finds, asymptotically, the optimal codebook reproduction distribution, within a constrained set of distributions satisfying a prescribed Markovian property, e.g., of the same order as the source, which achieves the minimum per letter coding rate while maintaining a specified distortion level.