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On Improvement of Speech Intelligibility and Quality: A Survey of Unsupervised Single Channel Speech Enhancement Algorithms

Nasir Saleem, Muhammad Irfan Khattak, Elena Verdú
2019 International Journal of Interactive Multimedia and Artificial Intelligence  
Bivariate EMD was used to decompose the complex noisy signal into complex-valued IMFs and all IMFs were segmented into short-time frames for processing.  ...  The grouping of segments is based on the frequency characteristics of unvoiced segments by considering thresholding and Bayesian classification. Kokkinakis et al.  ... 
doi:10.9781/ijimai.2019.12.001 fatcat:s4uyjqpezbddfexaas6dhaar3m

Speech Enhancement Modeling Towards Robust Speech Recognition System [article]

Urmila Shrawankar, V. M. Thakare
2013 arXiv   pre-print
In this contribution, speech enhancement system is introduced for enhancing speech signals corrupted by additive noise and improving the performance of Automatic Speech Recognizers in noisy conditions.  ...  Automatic speech recognition experiments show that replacing noisy speech signals by the corresponding enhanced speech signals leads to an improvement in the recognition accuracies.  ...  Spectral Subtraction: Spectral subtraction is a speech enhancement scheme based on a direct estimation of the short-time spectral magnitude of clean speech.  ... 
arXiv:1305.1426v1 fatcat:g35jkfvxwjdjjnhn6uw77xx5uu

Structural and affective aspects of music from statistical audio signal analysis

Shlomo Dubnov, Stephen McAdams, Roger Reynolds
2006 Journal of the American Society for Information Science and Technology  
Understanding and modeling human experience and emotional response when listening to music are important for better understanding of the stylistic choices in musical composition.  ...  The audio analysis was conducted on two recordings of an extended contemporary musical composition by one of the authors.  ...  The method of calculation is based on an estimation of spectral flatness of each individual component, considered as a scalar time signal, as explained in the Appendix.  ... 
doi:10.1002/asi.20429 fatcat:xb4nh2yfufcu3ikfxj2hsx5vji

A Review on Machine Learning for Audio Applications

Nagesh B, R V College of Engineering, Bengaluru, India., Dr. M. Uttara Kumari, R V College of Engineering, Bengaluru, India.
2021 Journal of University of Shanghai for Science and Technology  
In this paper, a review of the various algorithms used by researchers in the past has been described and gives the appropriate algorithm that can be used for the respective applications.  ...  It deals with the manipulation of the audio signals to achieve a task like filtering, data compression, speech processing, noise suppression, etc. which improves the quality of the audio signal.  ...  The estimation of an audio file's intrinsic SMR can be represented as a regression issue based on the concept of speechto-music ratio.  ... 
doi:10.51201/jusst/21/06508 fatcat:iwj523grmnfm3awyiks6ebncgm

Acoustic Echo Cancellation Postfilter Design Issues For Speech Recognition System [article]

Urmila Shrawankar, V M Thakare
2013 arXiv   pre-print
A disadvantage is that the input signal of the Acoustic echo cancellation (AEC) has a low signal-to-noise ratio (SNR).  ...  The main advantage of this approach is that the residual echo and noise suppression does not suffer from the existence of a strong acoustic echo component.  ...  Late Reverberant Spectral Variance Estimation It requires an estimator for the late reverberant spectral variance of the near-end speech signal.  ... 
arXiv:1305.1141v1 fatcat:zlvx5hokqzgv7gzpfl2ur7kbda

Single Channel Speech Enhancement Techniques in Spectral Domain

Arata Kawamura, Weerawut Thanhikam, Youji Iiguni
2012 ISRN Mechanical Engineering  
One of the most famous single channel speech enhancement techniques is the spectral subtraction method proposed by S.F. Boll in 1979.  ...  The results show that an adaptive speech enhancement method based on MAP estimation gives the best noise reduction capability in comparison to other speech enhancement methods presented in this paper.  ...  The spectral subtraction method [3] is one of the most popular methods among numerous noise reduction techniques in spectral domain.  ... 
doi:10.5402/2012/919234 fatcat:kryigy6zpbgobmi2237mfktqom

A Simplified Early Auditory Model with Application in Speech/Music Classification

Wei Chu, Benoit Champagne
2006 2006 Canadian Conference on Electrical and Computer Engineering  
The past decade has seen extensive research on audio classification and segmentation algorithms.  ...  In this paper, by introducing certain modifications we propose a simplified version of this model which is linear except for the calculation of the square-root value of the energy.  ...  that significant reductions in computational complexity can be achieved.  ... 
doi:10.1109/ccece.2006.277665 dblp:conf/ccece/ChuC06 fatcat:4s5qlg3ejfdstgcwctahez2gse

A Block-Based Linear MMSE Noise Reduction with a High Temporal Resolution Modeling of the Speech Excitation

Chunjian Li, Søren Vang Andersen
2005 EURASIP Journal on Advances in Signal Processing  
For resource-limited applications such as hearing aids, the performance-to-complexity trade-off can be conveniently adjusted by tuning the number of spectral components to be included in the estimate of  ...  The proposed algorithm improves the segmental SNR of the noisy signal by 13 dB for the white noise case with an input SNR of 0 dB.  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their many constructive suggestions, which have largely improved the presentation of our results.  ... 
doi:10.1155/asp.2005.2965 fatcat:2uxoww2qk5hxjeiqbpgsk3ysme

A simplified early auditory model with application in audio classification

Wei Chu, Benoit Champagne
2006 Canadian journal of electrical and computer engineering  
In this paper, certain modifications are introduced to develop a simplified version of this model which is linear except for the calculation of the square-root value of the energy.  ...  The past decade has seen extensive research on audio classification and segmentation algorithms.  ...  that significant reductions in computational complexity can be achieved.  ... 
doi:10.1109/cjece.2006.259178 fatcat:qatmi45745fhvglpngxommoxg4

Spectral Anticipations

Shlomo Dubnov
2006 Computer Music Journal  
Conclusion This paper described a new measure for evaluation of randomness in case of complex signals based on the notion of anticipation.  ...  contents is , ‫דצמבר‬ based on individual judgments of musical anticipation.  ... 
doi:10.1162/comj.2006.30.2.63 fatcat:ziddpfhtifbulngjvxtai4sdie

A parametric formulation of the generalized spectral subtraction method

Boh Lim Sim, Yit Chow Tong, J.S. Chang, Chin Tuan Tan
1998 IEEE Transactions on Speech and Audio Processing  
In this paper, two short-time spectral amplitude estimators of the speech signal are derived based on a parametric formulation of the original generalized spectral subtraction method.  ...  Based on the formulation, the speech spectral amplitude estimator is derived and optimized by minimizing the mean-square error (MSE) of the speech spectrum.  ...  Er for his helpful comments in the first draft of this work.  ... 
doi:10.1109/89.701361 fatcat:xdlhs4iwmrdhrfcv4ostc7e4oa

A Two-Sensor Noise Reduction System: Applications for Hands-Free Car Kit

Alexandre Guérin, Régine Le Bouquin-Jeannès, Gérard Faucon
2003 EURASIP Journal on Advances in Signal Processing  
Particular attention is focused on the estimation of the different spectral densities (noise and noisy signals power spectral densities) which are critical for the quality of the algorithm.  ...  Results on recorded signals are provided, showing the superiority of the two-sensor approach to single microphone techniques.  ...  CONCLUSION In this paper, we proposed a two-sensor noise reduction algorithm based on cross-spectral subtraction.  ... 
doi:10.1155/s1110865703305098 fatcat:j64265yjrzb43n7ugffpaxm5z4

Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features [article]

Anish Acharya
2018 arXiv   pre-print
So, using this general idea of the Musical Community we propose three frames to be considered and analyzed for feature extraction for each of the audio signal -- opening, stanzas and closing -- and it  ...  This uses a very basic general idea about the structure of the audio signal which is generally in the shape of a trapezium.  ...  I would like to thank Professor Padhraich Smyth for offering CS277 and making it fun and flexible-an open environment to learn and more importantly to think in a new way.  ... 
arXiv:1901.02053v1 fatcat:2nagd6kgqjhapktcyr75zutjye

Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement

Qin Yan, Saeed Vaseghi, Esfandiar Zavarehei, Ben Milner, Jonathan Darch, Paul White, Ioannis Andrianakis
2008 Computer Speech and Language  
This paper presents a speech enhancement method based on the tracking and denoising of the formants of a linear prediction (LP) model of the spectral envelope of speech and the parameters of a harmonic  ...  The HNM parameters for the excitation signal comprise; voiced/unvoiced decision, the fundamental frequency, the harmonics' amplitudes and the variance of the noise component of excitation.  ...  Acknowledgement We thank the UK's EPSRC for funding project No. GR/S30238/01.  ... 
doi:10.1016/j.csl.2007.06.002 fatcat:26sfaiximjee5p4cf2zxcb6yye

Music Identification System Using MPEG-7 Audio Signature Descriptors

Shingchern D. You, Wei-Hwa Chen, Woei-Kae Chen
2013 The Scientific World Journal  
This paper describes a multiresolution system based on MPEG-7 audio signature descriptors for music identification.  ...  Simulation results show that the proposed method II can achieve an accuracy of 99.4% for query inputs both inside and outside the database.  ...  Acknowledgment This work was supported in part by National Science Council of Taiwan through Grants NSC 94-2213-E-027-042 and 99-2221-E-027-097.  ... 
doi:10.1155/2013/752464 pmid:23533359 pmcid:PMC3606779 fatcat:spihetdylbfrvc7vli2fhmkjua
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