You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We propose an audio codec that addresses the low-delay requirements of some applications such as network music performance. The codec is based on the modified discrete cosine transform (MDCT) with very short frames and uses gain-shape quantization to preserve the spectral envelope. The short frame sizes required for low delay typically hinder the performance of transform codecs. However, at 96 kbit s and with only 4 ms algorithmic delay, the proposed codec out-performs the ULD codec operating at the same rate. The total complexity of the codec is small, at only 17 WMOPS for real-time operation at 48 kHz.

Cited Abstract(s):
[1]: In this paper we present and evaluate several concealment strategies for packet losses in the context of a low delay predictive audio coder. Our goal is to minimize the audible impact of a packet loss. The problem is that the predictive coder is backward adaptive, hence depending on past values. There is a predictor reset, but to increase coding efficiency, the distance between two resets is several hundred packets. Hence, not only the lost packet itself cannot be reconstructed, but the transmitted data up to the next reset does not result in an exact reconstruction of the audio signal. Our approach is to try to use as much information as possible from the still available data until the next reset, and to reconstruct an audio signal such that distortions are least objectionable. To compare different approaches, we conducted a listening test. The result is that an adaptive reconstruction filter works best in this context.

[2]: This paper proposes a versatile perceptual audio coding method that achieves high compression ratios and is capable of low encoding decoding delay. It accommodates a variety of source signals (including both music and speech) with different sampling rates. It is based on separating irrelevance and redundancy reductions into independent functional units. This contrasts traditional audio coding where both are integrated within the same subband decomposition. The separation allows for the independent optimization of the irrelevance and redundancy reduction units. For both reductions, we rely on adaptive filtering and predictive coding as much as possible to minimize the delay. A psycho-acoustically controlled adaptive linear filter is used for the irrelevance reduction, and the redundancy reduction is carried out by a predictive lossless coding scheme, which is termed weighted cascaded least mean squared (WCLMS) method. Experiments are carried out on a database of moderate size which contains mono-signals of different sampling rates and varying nature (music, speech, or mixed). They show that the proposed WCLMS lossless coder outperforms other competing lossless coders in terms of compression ratios and delay, as applied to the pre-filtered signal. Moreover, a subjective listening test of the combined pre-filter lossless coder and a state-of-the-art perceptual audio coder (PAC) shows that the new method achieves a comparable compression ratio and audio quality with a lower delay.
The Fraunhofer Ultra Low Delay (ULD) codec [2] is one of the only full-bandwidth codecs with an algorithmic delay comparable to the proposed codec. Its structure, however, is completely different from that of the proposed codec. ULD is based on time-domain linear prediction instead of the MDCT. It uses a pre-filter post-filter combination, whose parameters are transmitted in the bitstream, to shape the quantization noise. ULD frames are 128 samples with 128 samples of look-ahead, for a total algorithmic delay of 256 samples at 48 kHz (5.3 ms). One disadvantage of the linear-prediction approach is the difficulty of resynchronizing the decoder after a packet is lost [1] . In contrast, the proposed codec only uses inter-frame prediction for @math , so the decoder resynchronizes very quickly after packet loss. Changing the proposed codec to have completely independent packets would cost approximately 12 bits per frame.