A Frequency/Detector Pruning Approach for Loudness Estimation

Krishnamoorthi, Harish; Spanias, Andreas; Berisha, Visar

doi:10.1109/lsp.2009.2028413

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Goertzel and JM-Filter are dedicated to compute an arbitrary specific frequency (a single frequency) and not a subset of consecutive K frequencies as usually performed by input/output pruning FFT methods [22][23] [24]. According to [22] and [23] the only frequency that could be detected or monitored is the first one X (0) (k=0) and that is why input/output pruning FFTs will be excluded from our performance comparison study due to the increasing complexity associated with the computation of the desired k th frequency that is obtained by computing the first k outputs.…”

Section: Performance Results -Complexity and Accuracymentioning

confidence: 99%

The JM-Filter to Detect Specific Frequency in Monitored Signal

Jaber

Massicotte

2021

IEEE Trans. Signal Process.

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The Discrete Fourier Transform (DFT) is a mathematical procedure that stands at the center of the processing inside a Digital Signal Processor (DSP). It has been widely known and argued in relevant literature that the Fast Fourier Transform (FFT) is useless in detecting specific frequencies in a monitored signal of length N because most of the computed results are ignored. In this paper, we present an efficient FFT-based method to detect specific frequencies in a monitored signal, which will then be compared to the most frequently used method which is the recursive Goertzel algorithm that detects and analyses one selectable frequency component from a discrete signal. The proposed JM-Filter algorithm presents a reduction of iterations compared to the first and second order Goertzel algorithm by a factor of r, where r represents the radix of the JM-Filter. The obtained results are significant in terms of computational reduction and accuracy in fixed-point implementation. Gains of 15 dB and 19 dB in signal to quantization noise ratio (SQNR) were respectively observed for the proposed first and second order radix-8 JM-Filter in comparison to Goertzel algorithm.

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Section: Performance Results -Complexity and Accuracymentioning

confidence: 99%

The JM-Filter to Detect Specific Frequency in Monitored Signal

Jaber

Massicotte

2021

IEEE Trans. Signal Process.

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“…This process is computationally expensive and not adequate for practical applications. To this end, a few computationally efficient alternatives [10,11,12] were proposed. In [11], a pruning approach was described to evaluate the auditory model stages in a computationally efficient manner.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, a few computationally efficient alternatives [10,11,12] were proposed. In [11], a pruning approach was described to evaluate the auditory model stages in a computationally efficient manner. In [10], a hybrid approach to loudness estimation for sinusoidal signals was proposed to speed up the sinusoidal component selection task.…”

Section: Introductionmentioning

confidence: 99%

Sinusoidal component selection based on partial loudness criteria

Krishnamoorthi

Spanias

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Sinusoidal models are widely used in parametric speech and audio coding schemes. A common requirement in these applications is to select only a subset of components that provide the greatest perceptual benefit particularly at low bitrates. Usually, perceptual sinusoidal component selection algorithms make use of greedy algorithms that are computationally expensive. In this paper, we present a new algorithm that selects sinusoidal components based on the partial loudness model proposed by Moore & Glasberg. We compare the performance of the proposed algorithm in terms of perceptual benefit and computational complexity to other existing sinusoidal selection algorithms.

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