Low-Rate Farrow Structure with Discrete-Lowpass and Polynomial Support for Audio Resampling

Chinaev, Aleksej; Thune, Philipp; Enzner, Gerald

doi:10.23919/eusipco.2018.8553469

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…Architectures and optimizations in the literature are in general abstracted from the hardware which will be used for implementation [15]- [18], focusing on the properties of the resampling filters [19]- [22]. There are digital architectures with arbitrary resampling ratios which can be either synchronous [23]- [24], or asynchronous [25]- [27] depending on the implementation. Resampling algorithms and solutions are usually classified according to the desired conversion ratio R. Integer ratios are well suited for implementation with Cascaded Comb filters or Cascaded Integrator Comb (CIC) architectures which exploit factorization of the transfer function.…”

Section: Sampling Rate Conversion Architecturesmentioning

confidence: 99%

An Architecture for Real-Time Arbitrary and Variable Sampling Rate Conversion With Application to the Processing of Harmonic Signals

Guarch

Baudrenghien

Aróstegui

2020

IEEE Trans. Circuits Syst. I

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The paper presents a new solution for sampling rate conversion and processing of harmonic signals with known but possibly varying fundamental frequency. This problem is commonly found in particle accelerators, for tracking the beam signals whose revolution frequency varies during the acceleration ramp. It is also common among many other fields such as speech and music processing, removal of mechanical noises, filtering of biomedical recordings, active crack imaging, etc. The key element in the proposed solution is a new architecture for a Farrow-based resampler, in which the resampling ratio can take any value and can be modified continuously to follow the signal fundamental frequency. The combination of two complementary resamplers creates a processing region where signal synchronous processing is performed. The resampler architecture is optimized for modern FPGA features. It decouples the processing and sampling clocks, and uses a single processing (hardware) clock whose frequency remains fixed. The functional model was migrated to Xilinx System Generator and the overall performance is evaluated with an application that filters a periodic signal whose frequency follows a known linear ramp in the presence of additive white noise.

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Section: Sampling Rate Conversion Architecturesmentioning

confidence: 99%

An Architecture for Real-Time Arbitrary and Variable Sampling Rate Conversion With Application to the Processing of Harmonic Signals

Guarch

Baudrenghien

Aróstegui

2020

IEEE Trans. Circuits Syst. I

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“…Conventional SRC methods then produce the output signal at the converted rate by computing the necessary output samples consecutively. Signal processing techniques for SRC include polyphase structures allowing both integer and rational conversion ratios [29-31, 27, 32], the Smith-Gossett algorithm based on a tabulated windowed sinc function [22], and the combination of an integer-factor up-sampler and a low-order variable fractional-delay filter, or Farrow filter [33,34,24,25,35].…”

Section: Introductionmentioning

confidence: 99%

Giant FFTs for Sample-Rate Conversion

Välimäki¹,

Bilbao²

2023

J. Audio Eng. Soc.

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The audio industry uses several sample rates interchangeably, and high-quality sample-rate conversion is crucial. This paper describes a frequency-domain sample-rate conversion method that employs a single large ("giant") fast Fourier transform (FFT). Large FFTs, corresponding to the duration of a track or full-length album, are now extremely fast, with execution times on the order of a few seconds on standard commercially available hardware. The method first transforms the signal into the frequency domain, possibly using zero-padding. The key part of the technique modifies the length of the spectral buffer to change the ratio of the audio content to the Nyquist limit. For up-sampling, an appropriate number of zeros is inserted between the positive and negative frequencies. In down-sampling, the spectrum is truncated. Finally, the inverse FFT synthesizes a time-domain signal at the new sample rate. The proposed method does not result in surviving folded spectral images, which occur in some instances with timedomain methods. However, it causes ringing at the Nyquist limit, which can be suppressed by tapering the spectrum and by low-pass filtering. The proposed sample-rate conversion method is targeted to offline audio applications in which sound files need to be converted between sample rates at high quality.

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“…Here, SRO and STO are blindly estimated from just the available acoustic signals by different algorithms like coherence drift [13][14][15], maximum-likelihood [16,17], correlation-based [18][19][20], or double-cross-correlation processing (DXCP) [21]. Afterwards, raw signals are resampled by ASRC, e.g., [22][23][24].…”

Section: Introduction and Relation To Prior Workmentioning

confidence: 99%

A Double-cross-correlation Processor for Blind Sampling Rate Offset Estimation in Acoustic Sensor Networks

Chinaev

Thune

Enzner

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Distributed hardware of acoustic sensor networks bears inconsistency of local sampling frequencies, which is detrimental to signal processing. Fundamentally, sampling rate offset (SRO) nonlinearly relates the discrete-time signals acquired by different sensor nodes. As such, retrieval of SRO from the available signals requires nonlinear estimation, like double-cross-correlation processing (DXCP), and frequently results in biased estimation. SRO compensation by asynchronous sampling rate conversion (ASRC) on the signals then leaves an unacceptable residual. As a remedy to this problem, multistage procedures have been devised to diminish the SRO residual with multiple iterations of SRO estimation and ASRC over the entire signal. This paper converts the mechanism of offline multi-stage processing into a continuous feedback-control loop comprising a controlled ASRC unit followed by an online implementation of DXCPbased SRO estimation. To support the design of an optimum internal model control unit for this closed-loop system, the paper deploys an analytical dynamical model of the proposed online DXCP. The resulting control architecture then merely applies a single treatment of each signal frame, while efficiently diminishing SRO bias with time. Evaluations with both speech and Gaussian input demonstrate that the high accuracy of multi-stage processing is maintained at the low complexity of single-stage (open-loop) processing.

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Low-Rate Farrow Structure with Discrete-Lowpass and Polynomial Support for Audio Resampling

Cited by 10 publications

References 32 publications

An Architecture for Real-Time Arbitrary and Variable Sampling Rate Conversion With Application to the Processing of Harmonic Signals

An Architecture for Real-Time Arbitrary and Variable Sampling Rate Conversion With Application to the Processing of Harmonic Signals

Giant FFTs for Sample-Rate Conversion

A Double-cross-correlation Processor for Blind Sampling Rate Offset Estimation in Acoustic Sensor Networks

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