Identifying language from songs

Mukherjee, Himadri; Dhar, Ankita; Obaidullah, Sk Md; Santosh, K. C.; Phadikar, Santanu

doi:10.1007/s11042-020-10163-6

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Mukherjee et al [22] suggested a VAD method that utilizes the Line Spectral Frequencybased Statistical attributes called LSF-S merged with an extreme learning-based classifier. Initially, the weights were obtained via a gating network.…”

Section: Literature Surveymentioning

confidence: 99%

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

Selvaraj

Chandra

2022

IFS

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The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

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Section: Literature Surveymentioning

confidence: 99%

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

Selvaraj

Chandra

2022

IFS

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“…The automatic speech recognition (ASR) system is able to provide 100% accuracy under clean environment. But, its performance is degrades significantly when the spoken utterances gets contaminated by the presense of background noise or mismatch in acoustic features extracted from noisy or clean condtions [13,14,15] and mismatch in the labelled speech data used to train the classifier [16]. Hence, the performance of ASR system is constrained by two choices namely, correct labelling of speech data and selection of acoustic features.…”

Section: Related Workmentioning

confidence: 99%

Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

Mahadevaswamy

Ravì

2021

Preprint

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An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 75% of collected speech data is used for training the acoustic models and 25% of speech database is used for the system testing. The Performance of the system is presented interms of Word Error Rate (WER). Wavelet Packet Decomposition along with Mel filter bank is used to achieve feature extraction. The proposed feature extraction performs slightly better than the conventional features such as MFCC, PLP interms of WRA and WER under uncontrolled conditions. For the speech corpus collected in Kannada Language, the proposed features shows an improvement in WRA of 1.79% over baseline features.

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“…Mukherjee et al [22] suggested a VAD method that utilizes the Line Spectral Frequency- Initially, the weights were obtained via a gating network. After, the manifold DNNs and the gating network were trained together.…”

Section: Literature Surveymentioning

confidence: 99%

Ideal Ratio Mask Estimation using Supervised DNN Approach for Target Speech Signal Enhancement

Selvaraj

Chandra

2021

Preprint

View full text Add to dashboard Cite

The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But, this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

show abstract

Identifying language from songs

Cited by 3 publications

References 31 publications

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

Ideal Ratio Mask Estimation using Supervised DNN Approach for Target Speech Signal Enhancement

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