A Multichannel Sinusoidal Model Applied to Spot Microphone Signals for Immersive Audio

Tzagkarakis, Christos; Mouchtaris, Athanasios; Tsakalides, Panagiotis

doi:10.1109/tasl.2009.2021716

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…The remaining signal is the sinusoidal noise component , also referred to here as residual or sinusoidal error signal, which is the "stochastic" part of the audio signal, since it is very difficult to accurately model, but at the same time essential for high-quality audio synthesis. Accurately modeling the stochastic component has been examined both for the single-channel case, e.g., [2], [20], [21] and the multi-channel audio case [3]. Practically, after the sinusoidal parameters are estimated, the noise component is computed by subtracting the sinusoidal component from the original signal.…”

Section: Sinusoidal Modelmentioning

confidence: 99%

“…For the multichannel case, in each iteration, the algorithm picks a sinusoidal component frequency that is optimal for all channels, as well as channel-specific amplitudes and phases. This choice minimizes the perceptual distortion measure (3) where is the Fourier transform of the residual signal of the th channel after the th iteration, and is a frequency weighting function set as the inverse of the current masking threshold energy. The contributions of each channel are simply summed to obtain the final measure.…”

Section: B Multi-channel Sinusoidal Selectionmentioning

confidence: 99%

“…The sinusoidal model allows for a compact representation of the original signal and for efficient encoding and quantization. Extending the sinusoidal model to multi-channel audio applications has also been proposed (e.g., [3]). …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Griffin

Hirvonen

Tzagkarakis

et al. 2011

IEEE Trans. Audio Speech Lang. Process.

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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.

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Section: Sinusoidal Modelmentioning

confidence: 99%

Section: B Multi-channel Sinusoidal Selectionmentioning

confidence: 99%

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

Griffin

Hirvonen

Tzagkarakis

et al. 2011

IEEE Trans. Audio Speech Lang. Process.

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show abstract

“…Developed as a multichannel audio compression scheme, SAOC also aims at recovering so called sound objects at the decoding side from a transmitted downmix signal and side information about the audio objects. In the literature, different kinds of side information were also considered in the framework of Spatial Audio Coding (SAC), such as the inter and intrachannel correlation [21], spatial coherence cues [22], source localization parameters [23], or a sinusoids plus noise model of the sources [24]. In SAOC [20], high quality remixing is guaranteed by also transmitting perceptually-encoded residual signals resulting from an imperfect object extraction at the encoding side (therefore jointly exploiting waveform coding and parametric coding principles).…”

Section: Introductionmentioning

confidence: 99%

Coding-Based Informed Source Separation: Nonnegative Tensor Factorization Approach

Ozerov

Liutkus

Badeau

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Informed source separation (ISS) aims at reliably recovering sources from a mixture. To this purpose, it relies on the assumption that the original sources are available during an encoding stage. Given both sources and mixture, a sideinformation may be computed and transmitted along with the mixture, whereas the original sources are not available any longer. During a decoding stage, both mixture and side-information are processed to recover the sources. ISS is motivated by a number of specific applications including active listening and remixing of music, karaoke, audio gaming, etc. Most ISS techniques proposed so far rely on a source separation strategy and cannot achieve better results than oracle estimators. In this study, we introduce Coding-based ISS (CISS) and draw the connection between ISS and source coding. CISS amounts to encode the sources using not only a model as in source coding but also the observation of the mixture. This strategy has several advantages over conventional ISS methods. First, it can reach any quality, provided sufficient bandwidth is available as in source coding. Second, it makes use of the mixture in order to reduce the bitrate required to transmit the sources, as in classical ISS. Furthermore, we introduce Nonnegative Tensor Factorization as a very efficient model for CISS and report rate-distortion results that strongly outperform the state of the art.

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“…Tzagkarakis used a sinusoidal model and linear prediction to parameterize the separate spot microphone channels, then downmixed the residual signals. This coding scheme is more suitable for multichannel signals with weak correlation, and such scenarios require Independent channel decoding [16]. In 2010, Pinto et al utilized a space/time-frequency transform to decompose the WFS signals into plane waves and evanescent waves.…”

Section: Introductionmentioning

confidence: 99%

Expanded three-channel mid/side coding for three-dimensional multichannel audio systems

Dong

Wang

et al. 2014

J AUDIO SPEECH MUSIC PROC.

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Three-dimensional (3D) audio technologies are booming with the success of 3D video technology. The surge in audio channels makes its huge data unacceptable for transmitting bandwidth and storage media, and the signal compression algorithm for 3D audio systems becomes an important task. This paper investigates the conventional mid/side (M/S) coding method and discusses the signal correlation property of three-dimensional multichannel systems. Then based on the channel triple, a three-channel dependent M/S coding (3D-M/S) method is proposed to reduce interchannel redundancy and corresponding transform matrices are presented. Furthermore, a framework is proposed to enable 3D-M/S compress any number of audio channels. Finally, the masking threshold of the perceptual audio core codec is modified, which guarantees the final coding noise to meet the perceptual threshold constraint of the original channel signals. Objective and subjective tests with panning signals indicate an increase in coding efficiency compared to Independent channel coding and a moderate complexity increase compared to a PCA method.

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A Multichannel Sinusoidal Model Applied to Spot Microphone Signals for Immersive Audio

Cited by 7 publications

References 30 publications

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

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

Coding-Based Informed Source Separation: Nonnegative Tensor Factorization Approach

Expanded three-channel mid/side coding for three-dimensional multichannel audio systems

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