Variable Dimension Trellis-Coded Quantization of Sinusoidal Parameters

Christensen

IEEE Signal Process. Lett.

et al. 2009

We propose a new multiple description spherical quantization with repetitively coded amplitudes (MDSQRA) scheme suited for quantization of sinusoidal parameters. The quantization scheme is constituted by a set of spherical quantizers inspired by the multiple description spherical trellis-coded quantization (MDSTCQ) scheme. In this scheme, we apply repetitive coding on the amplitudes, while multiple description coding are applied on the phases and frequencies. Thereby, MDSQRA becomes directly implementable, as opposed to MDSTCQ, since the phase and frequency quantizers depend on the amplitudes which have dissimilar descriptions in MDSTCQ. Furthermore, we implement MDSQRA into a perceptual matching pursuit based sinusoidal audio coder. Finally, we evaluate MDSQRA through perceptual distortion measurements and MUSHRA listening tests. The tests show that MDSQRA outperforms MDSTCQ with respect to a expected perceptual distortion measure. The same results are obtained through the MUSHRA tests performed on sound clips coded using MDSQRA and MDSTCQ.Index Terms-Multiple description coding, perceptual audio coding, pre-and post-filtering, sinusoidal parametric coding, spherical quantization.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Parametric Audio Coding Using Multiple Description Coding

Christensen

IEEE Signal Process. Lett.

et al. 2009

IEEE Trans. Audio Speech Lang. Process.

“…Thus, it is expected that the average codeword length will be reduced after the Huffman coding. The average codeword length is defined as (14) where is the probability of occurrence for the th codeword, is the length of each codeword and is the total number of codewords, as is the number of bits assigned to each codeword before the Huffman encoding. Table II presents the percentages of compression that can be achieved through Huffman encoding for each audio signal for , , and bits of quantization.…”

Section: Quantization and Entropy Coding Of Random Samplesmentioning

confidence: 99%

“…It must be noted that all the aforementioned methods encode the sinusoidal parameters independently for each short-time segment of the audio signal. Extensions of these methods, where the sinusoidal parameters can be jointly quantized across neighboring segments, have recently been proposed, e.g., [14].…”

Section: Introductionmentioning

confidence: 99%

Single-Channel and Multi-Channel Sinusoidal Audio Coding Using Compressed Sensing

Griffin

Hirvonen

Tzagkarakis

et al. 2011

Abstract-Compressed sensing (CS) samples signals at a much lower rate than the Nyquist rate if they are sparse in some basis. In this paper, the CS methodology is applied to sinusoidally modeled audio signals. As this model is sparse by definition in the frequency domain (being equal to the sum of a small number of sinusoids), we investigate whether CS can be used to encode audio signals at low bitrates. In contrast to encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do, we propose encoding few randomly selected samples of the time-domain description of the sinusoidal component (per signal segment). The potential of applying compressed sensing both to single-channel and multi-channel audio coding is examined. The listening test results are encouraging, indicating that the proposed approach can achieve comparable performance to that of state-ofthe-art methods. Given that CS can lead to novel coding systems where the sampling and compression operations are combined into one low-complexity step, the proposed methodology can be considered as an important step towards applying the CS framework to audio coding applications.

“…The quantizers were designed to minimize a perceptual distortion measure subject to an entropy constraint based on a high-resolution assumption, i.e., a high number of bits per sinusoid whereby analytic expressions for the point densities of the quantizers were derived. In [5], the spherical quantizers of [4] were improved by joint quantization of the parameters of a variable number of sinusoids. Under a high-resolution assumption, the optimal point densities of the proposed quantization scheme, named Manuscript spherical trellis-coded quantization (STCQ), were derived for a given entropy.…”

Section: Introductionmentioning

confidence: 99%

Multiple Description Trellis-Coded Quantization of Sinusoidal Parameters

Larsen

Christensen

IEEE Trans. Signal Process.

2008

A new scheme for sinusoidal audio coding named multiple description spherical trellis-coded quantization is proposed and analytic expressions for the point densities and expected distortion of the quantizers are derived based on a high-resolution assumption. The proposed quantizers are of variable dimensions meaning that any number of sinusoids can be quantized jointly for each audio segment whereby a lower distortion is achieved compared to previously published scalar spherical quantizers. The quantizers are designed to minimize a perceptual distortion measure subject to an entropy constraint for a given packet-loss probability. In experiments, the performance of the quantizers is assessed and compared to the corresponding single description spherical quantizer and associated bounds under various conditions and is found to increase robustness towards packet-loss.