Seungkwon Beack

The increasing demand for high-quality audio content in applications such as media streaming and immersive audio services has driven the development of efficient audio coding techniques. Among these techniques, entropy coding is an essential component for the lossless compression of quantized spectral coefficients, which constitute the largest portion of the compressed audio bitstream. As the latest audio coding standard, Unified Speech and Audio Coding (USAC) has adopted ContextAdaptive Arithmetic Coding (CAAC) to employ its entropy coding method. Although CAAC can provide efficient lossless coding, it has several limitations, including bit overhead caused by variations in bit lengths between coefficients and considerable memory requirements for storing multiple context tables. To reduce excessive memory consumption, this study proposes Dynamic Bit-plane Arithmetic Coding (DBAC) as a new entropy coding method for quantized spectral coefficients. DBAC mitigates the bit overhead by using escape symbols for binarized coefficients and reduces memory usage while maintaining coding performance, even with marginal coding gains. In addition, the generation and selection methods of newly designed multiple contexts are also introduced to optimize arithmetic coding on the proposed dynamic bit-plane of DBAC. Experimental results demonstrate that the proposed method achieves 30% memory reduction with comparable coding performance compared to USAC.

Alias-and-Separate (AaS) speech coding framework has shown the possibility to encode wideband (WB) speech with a narrowband (NB) speech codec and reconstruct it using speech separation. WB speech is first decimated incurring aliasing and then coded, transmitted, and decoded with a NB codec. The decoded signal is then separated into lower band and spectrally-flipped high band using a speech separation module, which are expanded, lowpass/highpass filtered, and added together to reconstruct the WB speech. The original AaS system, however, has algorithmic delay originated from the overlap-add operation for consecutive segments. This algorithmic delay can be reduced by omitting the overlap-add procedure, but the quality of the reconstructed speech is also degraded due to artifacts on the segment boundaries. In this work, we propose an improved AaS framework with minimum algorithmic delay. The decoded signal is first expanded by inserting zeros in-between samples before being processed by source separation module. As the expanded signal can be viewed as a summation of the frequency-shifted versions of the original signal, the decoded-and-expanded signal is then separated into the frequency-shifted signals, which are multiplied by complex exponentials and summed up to reconstruct the original signal. With carefully designed transposed convolution operation in the separation module, the proposed system requires minimal algorithmic delay while preventing discontinuity at the segment boundaries. Additionally, we propose to employ a generative vocoder to further improve the perceived quality and a modified multi-resolution short-time Fourier transform (MR-STFT) loss. Experimental results on the WB speech coding with a NB codec demonstrated that the proposed system outperformed the original AaS system and the existing WB speech codec in the subjective listening test. We have also shown that the proposed method can be applied when the decimation factor is not 2in the experiment on the fullband speech coding with a WB codec.

Complex-valued frequency-domain linear predictive coding (CLPC) has been developed for audio coding. Recently, representations for efficiently quantizing CLPC coefficients have been proposed, including the complex immittance spectral frequency (CISF). The CISF has limitations in that it requires signalling the sequential information to eliminate ambiguity and the highest-order coefficient (HOC) for reconstructing the CLPC coefficients. This study developed a modified CISF-based method that eliminates the need for additional information by utilizing intermediate complex polynomial properties. Furthermore, a perceptual-metric-based codebook search was proposed to improve quantization efficiency. The experimental results show robust quantization performance, while listening tests demonstrate superior audio quality compared to MPEG-D USAC long TCX at 12 kbps.

This paper presents comprehensive optimizations of a neural audio coder built upon a variational autoencoder (VAE) system integrated with an arithmetic coder. Our optimizations focus on two primary aspects: a novel loss function design and advanced entropy modeling of bottleneck latent embeddings. The loss function design incorporates parameters from a psychoacoustic model (PAM) into the frame-wise distortion measure, providing excellent perceptual quality. In addition, a multi-time scale discriminator is utilized to minimize distortions across adjacent frames, reducing artifacts at frame edges. Also, the coder is optimized considering three sophisticated entropy models within the latent domain: the Factorized Entropy Model (FEM), the Hyperprior Model (HPM), and the Joint Hierarchical Model (JHM). Notably, the JHM enhances context modeling across frames to effectively predict components influenced by long-term dependencies. To verify the optimization performance, we conducted extensive experiments using a dataset consisting of commercial movie clips and two additional public datasets. Objective metrics consistently demonstrated that our optimized loss function and latent modeling achieved superior performance across all test datasets compared to traditional codecs such as LAME-MP3 and FDK-AAC. Subjective assessments also indicated that our system could offer comparable or superior auditory quality to FDK-AAC.

For decades, linear predictive (LP) analysis of the real-valued time-domain signals has been developed for speech coding, analysis, and synthesis. Recently, the complex-valued frequency-domain LP coding was developed to enhance the estimation performance of the temporal envelope. To apply it for audio coding, a suitable representation for the complex-valued frequency-domain LP coefficients (CLPC) is required before quantization, but there was no efficient way to represent it. To address the problem, we propose efficient CLPC representations that retain some useful properties of conventional LPC representations. Through quantitative and qualitative evaluations, we demonstrate that our proposed representations increase quantization efficiency and improve audio coding performance.

Over the past decade, audio coding technology has seen standardization and the development of many frameworks incorporated with linear predictive coding (LPC). As LPC reduces information in the frequency domain, LP-based frequency-domain noise-shaping (FDNS) was previously proposed. To code transient signals effectively, FDNS with temporal noise shaping (TNS) has emerged. However, these mainly operated in the modified discrete cosine transform domain, which essentially accompanies time domain aliasing. In this paper, a unified noise-shaping (UNS) framework including FDNS and complex LPC-based TNS (CTNS) in the DFT domain is proposed to overcome the aliasing issues. Additionally, a modified polar quantizer with phase contrast control is proposed, which saves phase bits depending on the frequency envelope information. The core coding feasibility at low bit rates is verified through various objective metrics and subjective listening evaluations.

This letter proposes a new method for audio coding that utilizes blind spectral recovery to improve the coding efficiency without compromising performance. The proposed method transmits only a fraction of the spectral coefficients, thereby reducing the coding bit rate. Then, it recovers the remaining coefficients in the decoder using the transmitted coefficients as input. The proposed method is differentiated from conventional spectral recovery in that the coefficients to be recovered are interleaved with the transmitted coefficients to obtain the most data correlation. Further, it enhances the transmitted coefficients, which are degraded by quantization errors, to deliver better information to the recovery process. The spectral recovery is conducted recursively on a band basis such that information recovered in one band is used for the recovery in subsequent bands. An improved level correction for the recovered coefficients and a new sign coding are also developed. A subjective performance evaluation confirms that the proposed method at 40 kbps provides statistically equivalent sound quality to a state-of-the-art coding method at 48 kbps for speech and music categories.