Probability distribution estimation of music signals in time and frequency domains

Arora, Vaibhav; Kumar, Ravi

doi:10.1109/icdsp.2014.6900696

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…As shown in Figure (2), the mean value (µ )have the highest value ,while the other elements (µ − 𝜎, µ − 2𝜎, µ + 𝜎, µ + 2𝜎) have smallest value,so its weight will be small depending on the value of Gaussian function, so the features will be selected in more suitable manner by giving the mean value the highest weight and the weight is decreased as moving from the mean according to the significance of the statistics, as shown in Figure 2, ( µ + 𝜎) and µ − 𝜎) have weight smaller than µ , also ( µ + 2𝜎) and µ − 2𝜎) have lowest values, this can increase the obtained information from using the mean only. The other contributions of this method (GWT) is proposed feature selection method based on statistics of each pool as described in the different Gaussian function in Figure 2, which is described the differences between there different Gaussian function values according to µ 𝑎𝑛𝑑 𝜎 [24][25][26].…”

Section: Methodsmentioning

confidence: 99%

A novel pooling layer based on gaussian function with wavelet transform

alhussainy

Jasim

2020

IJEECS

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<p>Convolution is represent the basic layer in the convolutional neural network, but it can results in very big size of the data at its output, so this data need to be reduced or down sampled to reduce the required operation in the network and increase its efficiency by choosing the most important features of the input signal. Different pooling methods was used to perform down sampling the size of these features. In this paper, we have proposed a novel pooling method by using Gaussian based Wavelet Transform and named as (GWT). Gaussian probability distribution function is used to determine the basic and most important statistics of the signal in each pooling size, and depending on this function, the basic statistics with the most priority is determined with their weight ,which are used as coefficients for wavelet filter to extract the pooling features . According to the procedure of extract the wavelet coefficients filter, Three method are proposed ,the first method (GWT1 ) is used the normalized values of basic statistics as weight to be multiplied by original signal, the second method (GWT2) used the determined statistics as features of the original signal and multiply it with constant weights based on half Gaussian ,while the third method (GWT3) is work in similar way to (GWT1) except that, it depend on entire signal instead of every pool size for calculation the basic statistics. Also the proposed methods are combined with other standard methods such as max and pooling. The experiments are performed on different types of dataset , two dimension (images) and one dimension (such as ECG signal), the results are compared with other methods and show that the proposed methods perform or outperform the other methods and can increase the performance of the (CNN).</p>

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Section: Methodsmentioning

confidence: 99%

A novel pooling layer based on gaussian function with wavelet transform

alhussainy

Jasim

2020

IJEECS

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“…with possible values lying within the interval [0; 1] and values close to zero corresponding to unclipped signal. More detailed information on features of parameter (2) can be found in [11] and some known speech distributions have been tested as hypotheses for different genres of music in [12]. As can be seen, 4 = 1 √ 4 ⁄ = 2 √ 4 ⁄ is signal variance normalized by the square root of the fourthorder central moment.…”

Section: Some Features Of Studied Parametersmentioning

confidence: 99%

Audio Signals Clipping Detection Using Kurtosis and Its Transforms

Prodeus¹,

Didkovska²

2020

IJC

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This paper compares the results of subjective and objective assessments of the quality of speech and music signals distorted during clipping when large instantaneous signal values are replaced by a certain threshold constant or by values close to it. It was proposed in recent works to use kurtosis and some of its simple functional transforms such as reciprocal of kurtosis and square root of reciprocal of kurtosis as objective (instrumental) clipping value measures. This paper clarifies the results of a subjective assessment of the quality of speech and music signals distorted by clipping. A comparison of the obtained estimates allows one to conclude that the human auditory system is slightly more sensitive to the clipping of musical signals than to the clipping of speech signals, but this difference is small. Similarly, objective quality measures of clipped signals are almost equally sensitive to the clipping value of speech and music signals. An analysis of the variability of the kurtosis estimates, depending on the time of estimation, showed that the relative standard deviation of the kurtosis estimates is close to 10% for the analysis time interval of 1–40 s.

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“…3 In such applications, one can expect that output of an unknown FIR system includes acoustic signals with a positive kurtosis such as human conversation, music or spike noise, that is, so called super-Gaussian acoustic signals. [10][11][12] It is especially important to assume such conditions in acoustic echo cancellers; it is pointed out that estimation of system coefficients becomes problematic when noise contaminates output of an unknown FIR system, and SNR (Signal to Noise Ratio) declines substantially. 13 In addition, most methods of FIR system identification deal only with unknown coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…Actual use of FIR system identification pertains to the field of acoustic signal processing such as acoustic echo cancellation and active noise control . In such applications, one can expect that output of an unknown FIR system includes acoustic signals with a positive kurtosis such as human conversation, music or spike noise, that is, so called super‐Gaussian acoustic signals . It is especially important to assume such conditions in acoustic echo cancellers; it is pointed out that estimation of system coefficients becomes problematic when noise contaminates output of an unknown FIR system, and SNR (Signal to Noise Ratio) declines substantially .…”

Section: Introductionmentioning

confidence: 99%

A nonparametric Bayesian model for system identification based on a super‐Gaussian distribution

Tanji

Murakami

Kamata

2019

Elect Comm in Japan

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In the acoustic signal processing applications of finite impulse response (FIR) system identification, it is important to develop the identification method that is robust to super‐Gaussian noises. Moreover, the identification method that estimates the FIR coefficients and the order of the unknown system is required, because the order of the unknown system is unavailable in advance. Therefore, in this paper, we propose a nonparametric Bayesian (NPB) model for FIR system identification using a super‐Gaussian likelihood and the beta‐Bernoulli process. In the proposed NPB model, we employ the hyperbolic secant distribution for the likelihood function. Then, we derive the inference algorithm to simultaneously estimate the FIR coefficients and the order of the unknown system. Our inference algorithm based on a hybrid inference approach combining the majorization‐minimization (MM) algorithm and the Gibbs sampler. The simulation results suggest that the proposed method outperforms the conventional identification algorithms in a super‐Gaussian noise environment.

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Probability distribution estimation of music signals in time and frequency domains

Cited by 11 publications

References 10 publications

A novel pooling layer based on gaussian function with wavelet transform

A novel pooling layer based on gaussian function with wavelet transform

Audio Signals Clipping Detection Using Kurtosis and Its Transforms

A nonparametric Bayesian model for system identification based on a super‐Gaussian distribution

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