Undermind Research Topic: I want to find academic research papers that apply the Hough transform to audio spectra to recognize the harmonic structure of tonal audio and determine the fundamental pitch.

Research topic

I want to find academic research papers that apply the Hough transform to audio spectra to recognize the harmonic structure of tonal audio and determine the fundamental pitch.

Report

Only paper [1] directly utilizes the Hough transform for harmonic detection in the time-frequency domain but is focused on military target noise rather than tonal audio for determining fundamental pitch.

Details:

[1] Harmonic Detection Arithmetic Based on Hough Transformation in Time-frequency Domain (2010):
- Utilizes the Hough transform for detecting harmonic signals, applying it to noise from military targets.
- Describes the application of the Hough transform to detect base frequencies and shifting rates in harmonic signal sets.
- Demonstrates the effectiveness of the Hough transform under low SNR conditions.
- Although beneficial for detecting harmonic structures, it focuses on military application rather than general tonal audio.
[2] Detection of chirp and other components in the time-frequency plane using the Hough and Radon transforms (1997):
- Proposes a Hough and Radon transform-based method for detecting chirp components in nonstationary signals.
- Is not focused on harmonic structure or pitch determination in tonal audio.
[3] Application of Harmonic Set (HS) Algorithm to the Helicopter Passive Acoustic Detection (2003):
- Discusses the detection of harmonically related tones using the Harmonic Set algorithm, not the Hough transform.
- Focuses on helicopter acoustic signal recognition.
Other References:
- [4, 5, 6, 7, 8, 9, 10] explore various methods and algorithms for pitch detection and harmonic analysis in audio but do not specifically apply the Hough transform. These methods include modifications of existing transforms like the Hilbert-Huang transform, Fourier transform techniques, and harmonic set algorithms, emphasizing different aspects of signal processing.

Conclusion:

The literature search yielded limited results specific to the application of the Hough transform for recognizing harmonic structures and determining fundamental pitch in tonal audio. The most relevant paper [1] applies the Hough transform in a different context, focusing on military target noise rather than general audio processing. No papers were found that directly address the high-level goal of applying the Hough transform to audio spectra for tonal audio harmonic structure recognition and fundamental pitch determination.

References

Last 5 years

Last 2 years

> 1 citation per year

> 5 citations per year

Topic Match

Cit./Year

Year

Paper

Paper Relevance Summary

100.0%

0.0

2010

[1] Harmonic Detection Arithmetic Based on Hough Transformation in Time-frequency Domain Wu Na Journal of Detection and Control 2010 - 0 citations - Show abstract - Cite 100.0% topic match

Utilizes the Hough transform for harmonic signal detection. Applies Hough transform to time-frequency domain for base frequency detection using harmonic wavelet transformation. Focuses on military target noise, not specifically on tonal audio harmonic structure or fundamental pitch determination in general audio processing.

5.3%

0.4

1997

[2] Detection of chirp and other components in the time-frequency plane using the Hough and Radon transforms S. Krishnan and R. Rangayyan 1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM. 10 Years Networking the Pacific Rim, 1987-1997 1997 - 12 citations - Show abstract - Cite 5.3% topic match

Detects chirp components using Hough and Radon transforms. Applies these transforms in the time-frequency plane for nonstationary signals. Focused on chirp detection rather than harmonic structure or fundamental pitch in tonal audio.

4.5%

0.0

2003

[3] Application of Harmonic Set (HS) Algorithm to the Helicopter Passive Acoustic Detection Wei Li Journal of Detection and Control 2003 - 0 citations - Show abstract - Cite 4.5% topic match

Discusses harmonic set detection for helicopter acoustic signal recognition. Emphasizes detection of harmonically related tones within a noisy power spectrum. Does not apply or mention the Hough transform in recognizing harmonic structures or determining fundamental pitch.

4.0%

0.3

2010

[4] Accurate Monophonic Pitch Tracking Algorithm for QBH and Microtone Research K. Akant, ..., and S. Limaye Journal Not Provided 2010 - 5 citations - Show abstract - Cite 4.0% topic match

Provides an algorithm for monophonic pitch detection. The method involves "Fourier of Fourier transform" and Parabolic Interpolation to enhance frequency resolution. Does not involve the Hough transform or focus on harmonic structure recognition.

3.6%

0.2

2009

[5] Fundamental frequency estimation for music signals with modified Hilbert-Huang transform (HHT) EnShuo Tsau, ..., and C.-C. Jay Kuo 2009 IEEE International Conference on Multimedia and Expo 2009 - 3 citations - Show abstract - Cite 3.6% topic match

Proposes a modified Hilbert-Huang transform (HHT) for pitch estimation. Focuses on real-time fundamental frequency detection using short temporal windows in music signals. Does not mention the Hough transform or harmonic structure analysis; hence, not directly relevant to the desired topic.

3.4%

0.0

2007

[6] A new strategy on pitch estimation in polyphonic musical audio Karin Dressler Journal of the Acoustical Society of America 2007 - 0 citations - Show abstract - Cite 3.4% topic match

Proposes a method for pitch estimation in polyphonic audio. Uses spectral peaks and harmonic relationships to derive fundamental frequencies. Does not specifically mention the application of the Hough transform.

3.2%

1.5

2001

[7] AN EFFICIENT PITCH-TRACKING ALGORITHM USING A COMBINATION OF FOURIER TRANSFORMS Sylvain Marchand Journal Not Provided 2001 - 35 citations - Show abstract - Cite 3.2% topic match

Detects pitch using a combination of Fourier transforms. Utilizes an enhanced Fourier transform and pitch tracking for accuracy and robustness. Does not mention the Hough transform; may be less relevant to harmonic structure identification.

3.1%

1.9

2018

[8] On the Estimation of Fundamental Frequency From Nonstationary Noisy Speech Signals Based on the Hilbert–Huang Transform L. Zão and Rosângela Coelho IEEE Signal Processing Letters 2018 - 13 citations - Show abstract - Cite 3.1% topic match

Provides a method to estimate fundamental frequency using the Hilbert–Huang transform. Analyzes speech signals with ensemble empirical mode decomposition and Hilbert transform to extract pitch information. Does not apply the Hough transform or address harmonic structure recognition in tonal audio.

2.9%

0.1

2005

[9] Automatic recognition of harmonic bird sounds Jason R. Heller and J. Pinezich Journal of the Acoustical Society of America 2005 - 1 citations - Show abstract - Cite 2.9% topic match

Provides a method for automatically recognizing harmonic bird sounds. Transforms sound into a spectrogram, extracts harmonics as curves, and determines harmonic relationships. Does not specify using the Hough transform, making its relevance uncertain for the desired topic on fundamental pitch detection.

2.8%

0.0

2011

[10] MULTIPLE FUNDAMENTAL FREQUENCY EXTRACTION FOR MIREX 2011 Karin Dressler Journal Not Provided 2011 - 0 citations - Show abstract - Cite 2.8% topic match

2.7%

0.5

2014

[11] A Pitch Salience Function Derived from Harmonic Frequency Deviations for Polyphonic Music Analysis Alessio Degani, ..., and Geoffroy Peeters International Conference on Digital Audio Effects 2014 - 5 citations - Show abstract - Cite 2.7% topic match

Estimates peak likelihoods as harmonic frequencies. Compares deviations from equal-tempered scale pitch frequencies to predict harmonics. Does not mention the use of the Hough transform.

2.7%

0.1

2013

[12] Comparative F0 Algorithms Based on Frequency Analysis Unidad Profesional, ..., and Gustavo A. Madero Journal Not Provided 2013 - 1 citations - Show abstract - Cite 2.7% topic match

Provides a comparison of fundamental pitch detection algorithms. Focuses on time-domain (autocorrelation), frequency-domain (Cepstrum, HPS), and joint approaches (Wavelet). Does not mention use of the Hough transform, making it less relevant to the specific topic.

2.6%

0.0

2013

[13] Pitch Detection Method Based on HHT Yao Wang, ..., and Tao Lei Advanced Materials Research 2013 - 0 citations - Show abstract - Cite 2.6% topic match

2.5%

0.0

1992

[14] Fundamental frequency tracking in the log frequency domain based on a pattern recognition algorithm Judith C. Brown and M. Puckette Journal of the Acoustical Society of America 1992 - 1 citations - Show abstract - Cite - PDF 2.5% topic match

2.5%

0.9

1975

[15] A semiautomatic pitch detector (SAPD) C. McGonegal, ..., and A. Rosenberg IEEE Transactions on Acoustics, Speech, and Signal Processing 1975 - 45 citations - Show abstract - Cite 2.5% topic match

2.4%

0.0

2007

[16] Robust Pitch Tracking of Acoustic Signals based on Local Periodicity Index Roudra Chakraborty and D. Sengupta Journal Not Provided 2007 - 0 citations - Show abstract - Cite 2.4% topic match

Uses variance minimization for pitch tracking. Minimizes variance of locally periodic subsamples to estimate fundamental frequency. No mention of Hough transform or harmonic structure recognition.

2.4%

12.1

1976

[17] A comparative performance study of several pitch detection algorithms L. Rabiner, ..., and C. McGonegal IEEE Transactions on Acoustics, Speech, and Signal Processing 1976 - 581 citations - Show abstract - Cite - PDF 2.4% topic match

2.3%

1.7

2012

[18] MULTIPLE FUNDAMENTAL FREQUENCY EXTRACTION FOR MIREX 2012 Karin Dressler Journal Not Provided 2012 - 22 citations - Show abstract - Cite 2.3% topic match

2.3%

11.3

1974

[19] Average magnitude difference function pitch extractor M. Ross, ..., and H. Manley IEEE Transactions on Acoustics, Speech, and Signal Processing 1974 - 568 citations - Show abstract - Cite 2.3% topic match

2.3%

0.4

2008

[20] Application of the fan-chirp transform to hybrid sinusoidal+noise modeling of polyphonic audio M. Bartkowiak 2008 16th European Signal Processing Conference 2008 - 7 citations - Show abstract - Cite 2.3% topic match

2.2%

2.3

2006

[21] Pseudo Pitch Synchronous Analysis of Speech With Applications to Speaker Recognition Ran D. Zilca, ..., and G. Ramaswamy IEEE Transactions on Audio, Speech, and Language Processing 2006 - 41 citations - Show abstract - Cite Abstract: The fine spectral structure related to pitch information is conveyed in Mel cepstral features, with variations in pitch causing variations in the features. For speaker recognition systems, this phenomenon, known as “pitch mismatch” between training and testing, can increase error rates. Likewise, pitch-related variability may potentially increase error rates in speech recognition systems for languages such as English in which pitch does not carry phonetic information. In addition, for both speech recognition and speaker recognition systems, the parsing of the raw speech signal into frames is traditionally performed using a constant frame size and a constant frame offset, without aligning the frames to the natural pitch cycles. As a result the power spectral estimation that is done as part of the Mel cepstral computation may include artifacts. Pitch synchronous methods have addressed this problem in the past, at the expense of adding some complexity by using a variable frame size and/or offset. This paper introduces Pseudo Pitch Synchronous (PPS) signal processing procedures that attempt to align each individual frame to its natural cycle and avoid truncation of pitch cycles while still using constant frame size and frame offset, in an effort to address the above problems. Text independent speaker recognition experiments performed on NIST speaker recognition tasks demonstrate a performance improvement when the scores produced by systems using PPS are fused with traditional speaker recognition scores. In addition, a better distribution of errors across trials may be obtained for similar error rates, and some insight regarding of role of the fundamental frequency in speaker recognition is revealed. Speech recognition experiments run on the Aurora-2 noisy digits task also show improved robustness and better accuracy for extremely low signal-to-noise ratio (SNR) data. 2.2% topic match

2.2%

1.9

1979

[22] Predicting musical pitch from component frequency ratios M. Piszczalski and B. Galler Journal of the Acoustical Society of America 1979 - 86 citations - Show abstract - Cite 2.2% topic match

2.2%

1.2

2006

[23] Real-Time Time-Domain Pitch Tracking Using Wavelets R. Maddox and E. Larson Journal Not Provided 2006 - 23 citations - Show abstract - Cite Abstract: A pitch tracker based on the fast lifting wavelet transform (FLWT) is developed in MATLAB and C++. Emphasis is placed on low latency (~25 ms), high time resolution, and accuracy. The pitch tracking algorithm implements the FLWT using the Haar wavelet, a transform which is shown to be mathematically equivalent to splitting a signal into low-pass and high-pass components and downsampling (generating approximations and details, respectively). Approximations are used in combination with intelligent peak detection to determine the pitch of vocal samples. An averaging scheme is employed to provide enhanced frequency resolution. This algorithm was tested on natural and synthetic signals—including a female vocal sample and two sets of generated sinusoidal signals—to determine its performance characteristics. Overall performance exceeded expectations, with most errors made in voiced/unvoiced detection. I. Background Natural sounds are a composition of a fundamental frequency with a set of harmonics which occur at near integer multiples of that fundamental. The frequency that the human ear interprets as the pitch of a sound is this fundamental frequency, even if it is absent in the sound. The pitch (or fundamental frequency) of natural sounds is important in many contexts. Pitch is largely responsible for inflections in human speech that carry conversational cues (e.g. discriminating between the spoken phrases “you are going to the store.” and “you are going to the store?”). These inflections also play a role in allowing us to consistently identify a given speaker. In addition to speech, musical compositions are typically sets of 1 Although there are classical distinctions between pitch and fundamental frequency, the two will be considered equivalent for the purposes of this paper. organized pitches. These pitches, in combination with their timing, enable us to distinguish Beethoven’s third symphony from Led Zeppelin’s “Stairway to Heaven”. Pitch tracking, then, is useful for musical analysis as well as for speech analysis. General pitch tracking is used in speech recognition and identification schemes as well as in the automated transcription of music. Real-time pitch tracking can be used as an aide for (or judge of) musical performers, revealing intricacies of performance not immediately revealed by the ear. Tracking the pitch of natural sounds— here the focus is primarily on speech—is not a trivial task. The topic of pitch tracking has been well explored, and there are several established pitch tracking methods available today, alongside several unconventional ones. All pitch trackers have certain advantages and disadvantages. 2.2% topic match

2.1%

0.0

2007

[24] Effects of Time and Frequency Resolution on Nonnegative Matrix Factorization Seng Sophea and S. Phon-Amnuaisuk 2007 International Symposium on Information Technology Convergence (ISITC 2007) 2007 - 0 citations - Show abstract - Cite 2.1% topic match

2.1%

2.0

1995

[25] Harmonics tracking and pitch extraction based on instantaneous frequency Toshihiko Abe, ..., and S. Imai 1995 International Conference on Acoustics, Speech, and Signal Processing 1995 - 58 citations - Show abstract - Cite 2.1% topic match

2.1%

0.0

2007

[26] SD / 0 70 11 77 v 2 29 J an 2 00 7 Robust Pitch Tracking of Acoustic Signals based on Local Periodicity Index Roudra Chakraborty Journal Not Provided 2007 - 0 citations - Show abstract - Cite 2.1% topic match

2.1%

45.9

2001

[27] Discrete-Time Speech Signal Processing: Principles and Practice T. Quatieri Journal Not Provided 2001 - 1057 citations - Show abstract - Cite Abstract: (NOTE: Each chapter begins with an introduction and concludes with a Summary, Exercises and Bibliography.) 1. Introduction. Discrete-Time Speech Signal Processing. The Speech Communication Pathway. Analysis/Synthesis Based on Speech Production and Perception. Applications. Outline of Book. 2. A Discrete-Time Signal Processing Framework. Discrete-Time Signals. Discrete-Time Systems. Discrete-Time Fourier Transform. Uncertainty Principle. z-Transform. LTI Systems in the Frequency Domain. Properties of LTI Systems. Time-Varying Systems. Discrete-Fourier Transform. Conversion of Continuous Signals and Systems to Discrete Time. 3. Production and Classification of Speech Sounds. Anatomy and Physiology of Speech Production. Spectrographic Analysis of Speech. Categorization of Speech Sounds. Prosody: The Melody of Speech. Speech Perception. 4. Acoustics of Speech Production. Physics of Sound. Uniform Tube Model. A Discrete-Time Model Based on Tube Concatenation. Vocal Fold/Vocal Tract Interaction. 5. Analysis and Synthesis of Pole-Zero Speech Models. Time-Dependent Processing. All-Pole Modeling of Deterministic Signals. Linear Prediction Analysis of Stochastic Speech Sounds. Criterion of "Goodness". Synthesis Based on All-Pole Modeling. Pole-Zero Estimation. Decomposition of the Glottal Flow Derivative. Appendix 5.A: Properties of Stochastic Processes. Random Processes. Ensemble Averages. Stationary Random Process. Time Averages. Power Density Spectrum. Appendix 5.B: Derivation of the Lattice Filter in Linear Prediction Analysis. 6. Homomorphic Signal Processing. Concept. Homomorphic Systems for Convolution. Complex Cepstrum of Speech-Like Sequences. Spectral Root Homomorphic Filtering. Short-Time Homomorphic Analysis of Periodic Sequences. Short-Time Speech Analysis. Analysis/Synthesis Structures. Contrasting Linear Prediction and Homomorphic Filtering. 7. Short-Time Fourier Transform Analysis and Synthesis. Short-Time Analysis. Short-Time Synthesis. Short-Time Fourier Transform Magnitude. Signal Estimation from the Modified STFT or STFTM. Time-Scale Modification and Enhancement of Speech. Appendix 7.A: FBS Method with Multiplicative Modification. 8. Filter-Bank Analysis/Synthesis. Revisiting the FBS Method. Phase Vocoder. Phase Coherence in the Phase Vocoder. Constant-Q Analysis/Synthesis. Auditory Modeling. 9. Sinusoidal Analysis/Synthesis. Sinusoidal Speech Model. Estimation of Sinewave Parameters. Synthesis. Source/Filter Phase Model. Additive Deterministic-Stochastic Model. Appendix 9.A: Derivation of the Sinewave Model. Appendix 9.B: Derivation of Optimal Cubic Phase Parameters. 10. Frequency-Domain Pitch Estimation. A Correlation-Based Pitch Estimator. Pitch Estimation Based on a "Comb Filter<170. Pitch Estimation Based on a Harmonic Sinewave Model. Glottal Pulse Onset Estimation. Multi-Band Pitch and Voicing Estimation. 11. Nonlinear Measurement and Modeling Techniques. The STFT and Wavelet Transform Revisited. Bilinear Time-Frequency Distributions. Aeroacoustic Flow in the Vocal Tract. Instantaneous Teager Energy Operator. 12. Speech Coding. Statistical Models of Speech. Scaler Quantization. Vector Quantization (VQ). Frequency-Domain Coding. Model-Based Coding. LPC Residual Coding. 13. Speech Enhancement. Introduction. Preliminaries. Wiener Filtering. Model-Based Processing. Enhancement Based on Auditory Masking. Appendix 13.A: Stochastic-Theoretic parameter Estimation. 14. Speaker Recognition. Introduction. Spectral Features for Speaker Recognition. Speaker Recognition Algorithms. Non-Spectral Features in Speaker Recognition. Signal Enhancement for the Mismatched Condition. Speaker Recognition from Coded Speech. Appendix 14.A: Expectation-Maximization (EM) Estimation. Glossary.Speech Signal Processing.Units.Databases.Index.About the Author. 2.1% topic match

2.0%

10.9

2001

[28] Weighted autocorrelation for pitch extraction of noisy speech T. Shimamura and Hajime Kobayashi IEEE Trans. Speech Audio Process. 2001 - 253 citations - Show abstract - Cite 2.0% topic match

2.0%

0.1

2011

[29] Adaptive harmonic decomposition using shift-invariant PLCA Benoit Fuentes, ..., and G. Richard Journal Not Provided 2011 - 1 citations - Show abstract - Cite 2.0% topic match

2.0%

7.2

1975

[30] Comparative performance study of several pitch detection algorithms M. J. Cheng, ..., and C. McGonegal Journal of the Acoustical Society of America 1975 - 355 citations - Show abstract - Cite - PDF 2.0% topic match

2.0%

0.5

2002

[31] Pitch detection of musical sounds using adaptive comb filters controlled by time delay Y. Tadokoro, ..., and M. Yamaguchi Proceedings. IEEE International Conference on Multimedia and Expo 2002 - 10 citations - Show abstract - Cite 2.0% topic match

2.0%

1.0

2004

[32] From Sound Sampling To Song Sampling J. Aucouturier, ..., and P. Hanappe International Society for Music Information Retrieval Conference 2004 - 20 citations - Show abstract - Cite 2.0% topic match

2.0%

3.5

2000

[33] Robust Multipitch Estimation for the Analysis and Manipulation of Polyphonic Musical Signals Anssi Klapuri Journal Not Provided 2000 - 87 citations - Show abstract - Cite 2.0% topic match

1.9%

1.9

2010

[34] FAN CHIRP TRANSFORM FOR MUSIC REPRESENTATION Pablo Cancela, ..., and Martín Rocamora Journal Not Provided 2010 - 28 citations - Show abstract - Cite 1.9% topic match

1.9%

0.1

2007

[35] AUTOMATIC TRANSCRIPTION OF POLYPHONIC PIANO MUSIC USING A NOTE MASKING TECHNIQUE Ronan Kelly and Jacqueline Walker Journal Not Provided 2007 - 1 citations - Show abstract - Cite 1.9% topic match

1.9%

1.4

2020

[36] Multi-Pitch Estimation of Polyphonic Music Based on Pseudo Two-Dimensional Spectrum Weiwei Zhang, ..., and F. Yin IEEE/ACM Transactions on Audio, Speech, and Language Processing 2020 - 7 citations - Show abstract - Cite 1.9% topic match

1.9%

0.2

2015

[37] Virtual Music Teacher for New Music Learners with Optical Music Recognition Viet-Khoi Pham, ..., and M. Tran Interacción 2015 - 2 citations - Show abstract - Cite - PDF 1.9% topic match

1.9%

0.0

2010

[38] Statistical analysis of speech signals by weighted ASMDF Roudra Chakraborty, ..., and D. Sengupta Journal Not Provided 2010 - 0 citations - Show abstract - Cite 1.9% topic match

1.9%

0.4

2006

[39] A Time-Frequency Analysis Method for Non-stationary Signals Based on Improved Hilbert-Huang Transform and Its Application Li Jia-qiang Journal of Shanghai Jiaotong University 2006 - 7 citations - Show abstract - Cite 1.9% topic match

1.9%

0.1

2014

[40] Um modelo de classificação supervisionada com rotulagem automática para reconhecimento de áudio de web rádios. D. Rezende Journal Not Provided 2014 - 1 citations - Show abstract - Cite 1.9% topic match

1.8%

0.1

2010

[41] A Threshold Adaptation based Voice Query Transcription Scheme for Music Retrieval Byeong-jun Han, ..., and Eenjun Hwang The Transactions of the Korean Institute of Electrical Engineers 2010 - 2 citations - Show abstract - Cite 1.8% topic match

1.8%

0.8

2003

[42] Using structure patterns of temporal and spectral feature in audio similarity measure Rui Cai, ..., and HongJiang Zhang Proceedings of the eleventh ACM international conference on Multimedia 2003 - 16 citations - Show abstract - Cite - PDF 1.8% topic match

1.8%

0.0

2009

[43] Multi-frequency Cyclostationary Analysis of Harmonic Signal of Helicopter Chang Jian-bin Journal of Detection and Control 2009 - 0 citations - Show abstract - Cite 1.8% topic match

1.8%

9.1

2004

[44] On the use of phase and energy for musical onset detection in the complex domain J. Bello, ..., and M. Sandler IEEE Signal Processing Letters 2004 - 186 citations - Show abstract - Cite 1.8% topic match

1.8%

0.1

1994

[45] The application of Bayesian inference to linear prediction of speech Gmk Saleh, ..., and W. Fitzgerald Journal Not Provided 1994 - 2 citations - Show abstract - Cite 1.8% topic match

1.8%

0.1

2005

[46] A Connectionist Model of Finding Partial Groups in Music Recordings with Application to Music Transcription M. Marolt https://doi.org/10.1007/3-211-27389-1_119 2005 - 2 citations - Show abstract - Cite 1.8% topic match

1.8%

68.6

1997

[47] Speaker recognition: a tutorial J.P. Campbell Proc. IEEE 1997 - 1868 citations - Show abstract - Cite - PDF 1.8% topic match

1.8%

82.1

1997

[48] Statistical methods for speech recognition F. Jelinek https://doi.org/10.2307/2670189 1997 - 2290 citations - Show abstract - Cite 1.8% topic match

1.8%

8.3

1999

[49] Speech processing and synthesis toolboxes D. Childers and J. A. Diaz https://doi.org/10.1121/1.1318896 1999 - 210 citations - Show abstract - Cite - PDF 1.8% topic match

1.8%

0.2

2019

[50] Propeller Shaft Frequency Extraction Based on the Improved Cyclic Modulation Spectrum Xingyue Zhou, ..., and Yukun Zhang OCEANS 2019 - Marseille 2019 - 1 citations - Show abstract - Cite 1.8% topic match

1.8%

0.2

2008

[51] On the estimation of pitch of noisy speech based on time and frequency domain representations C. Shahnaz, ..., and M. Ahmad 2008 Canadian Conference on Electrical and Computer Engineering 2008 - 3 citations - Show abstract - Cite 1.8% topic match

1.7%

1.7

1994

[52] A probabilistic approach to AMDF pitch detection Goangshiuan S. Ying, ..., and C. D. Mitchell Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96 1994 - 53 citations - Show abstract - Cite - PDF 1.7% topic match

1.7%

11.4

2003

[53] Pitch Extraction and Fundamental Frequency: History and Current Techniques David Gerhard Journal Not Provided 2003 - 249 citations - Show abstract - Cite 1.7% topic match

1.7%

0.9

2005

[54] Combination of autocorrelation-based features and projection measure technique for speaker identification Kuo-Hwei Yuo, ..., and Hsiao-Chuan Wang IEEE Trans. Speech Audio Process. 2005 - 18 citations - Show abstract - Cite 1.7% topic match

1.7%

13.2

2021

[55] CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions Ali Bou Nassif, ..., and K. Hirose ArXiv 2021 - 50 citations - Show abstract - Cite - PDF 1.7% topic match

1.7%

0.4

2020

[56] Retrieving Vocal-Tract Resonance and anti-Resonance From High-Pitched Vowels Using a Rahmonic Subtraction Technique Zhao Zhang, ..., and Jianguo Wei ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 - 2 citations - Show abstract - Cite 1.7% topic match

1.7%

0.0

2010

[57] Comparison among the capabilities of three algorithms on detection of harmonic set signal Tang Yu Journal Not Provided 2010 - 0 citations - Show abstract - Cite 1.7% topic match

1.7%

1.4

2000

[58] Employing Symmetry Constraints for Improved Frequency Estimation by Eigenanalysis Methods G. Smyth Technometrics 2000 - 35 citations - Show abstract - Cite 1.7% topic match

1.7%

0.0

2013

[59] ASR system based on pitch, energy contours and unvoiced regions V. Gupta and P. Das 2013 International Conference Oriental COCOSDA held jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE) 2013 - 0 citations - Show abstract - Cite 1.7% topic match

1.7%

6.7

2011

[60] KARMA: Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking Daryush D. Mehta, ..., and P. Wolfe The Journal of the Acoustical Society of America 2011 - 90 citations - Show abstract - Cite - PDF 1.7% topic match

1.7%

0.0

2010

[61] Monaural Speech Segregation in Reverberant Environments Zhaozhang Jin Journal Not Provided 2010 - 0 citations - Show abstract - Cite 1.7% topic match

1.7%

1.2

2018

[62] Formant priority channel selection for an "n-of-m" sound processing strategy for cochlear implants. Juliana N. Saba, ..., and J. Hansen The Journal of the Acoustical Society of America 2018 - 7 citations - Show abstract - Cite - PDF 1.7% topic match

1.7%

0.3

2003

[63] An Image-Based System for Spoken-Letter Recognition K. Saeed and Marcin Kozlowski International Conference on Computer Analysis of Images and Patterns 2003 - 7 citations - Show abstract - Cite 1.7% topic match

1.7%

0.5

2021

[64] Formant estimation from speech signal using the magnitude spectrum modified with group delay spectrum Husne Ara Chowdhury and Mohammad Shahidur Rahman Acoustical Science and Technology 2021 - 2 citations - Show abstract - Cite - PDF 1.7% topic match

1.7%

4.4

2018

[65] Voice Disorder Detection via an m-Health System: Design and Results of a Clinical Study to Evaluate Vox4Health U. Cesari, ..., and Laura Verde BioMed Research International 2018 - 28 citations - Show abstract - Cite - PDF Abstract: Objectives The current study presents a clinical evaluation of Vox4Health, an m-health system able to estimate the possible presence of a voice disorder by calculating and analyzing the main acoustic measures required for the acoustic analysis, namely, the Fundamental Frequency, jitter, shimmer, and Harmonic to Noise Ratio. The acoustic analysis is an objective, effective, and noninvasive tool used in clinical practice to perform a quantitative evaluation of voice quality. Materials and Methods A clinical study was carried out in collaboration with medical staff of the University of Naples Federico II. 208 volunteers were recruited (mean age, 44.2 ± 13.9 years), 58 healthy subjects (mean age, 36.7 ± 13.3 years) and 150 pathological ones (mean age, 47 ± 13.1 years). The evaluation of Vox4Health was made in terms of classification performance, i.e., sensitivity, specificity, and accuracy, by using a rule-based algorithm that considers the most characteristic acoustic parameters to classify if the voice is healthy or pathological. The performance has been compared with that achieved by using Praat, one of the most commonly used tools in clinical practice. Results Using a rule-based algorithm, the best accuracy in the detection of voice disorders, 72.6%, was obtained by using the jitter or shimmer value. Moreover, the best sensitivity is about 96% and it was always obtained by using jitter. Finally, the best specificity was achieved by using the Fundamental Frequency and it is equal to 56.9%. Additionally, in order to improve the classification accuracy of the next version of the Vox4Health app, an evaluation by using machine learning techniques was conducted. We performed some preliminary tests adopting different machine learning techniques able to classify the voice as healthy or pathological. The best accuracy (77.4%) was obtained by the Logistic Model Tree algorithm, while the best sensitivity (99.3%) was achieved using the Support Vector Machine. Finally, Instance-based Learning performed the best specificity (36.2%). Conclusions Considering the achieved accuracy, Vox4Health has been considered by the medical experts as a “good screening tool” for the detection of voice disorders in its current version. However, this accuracy is improved when machine learning classifiers are considered rather than the rule-based algorithm. 1.7% topic match

1.6%

2.0

2000

[66] A new strategy of formant tracking based on dynamic programming Kun Xia and C. Espy-Wilson Interspeech 2000 - 48 citations - Show abstract - Cite 1.6% topic match

1.6%

0.5

1997

[67] Techniques for Harmonic Sinusoidal Coding D. Rowe Journal Not Provided 1997 - 14 citations - Show abstract - Cite 1.6% topic match

1.6%

1.0

1998

[68] Robust word recognition using threaded spectral peaks B. Strope and A. Alwan Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181) 1998 - 26 citations - Show abstract - Cite 1.6% topic match

1.6%

0.2

2013

[69] Pitch estimation of Marathi spoken numbers in various speech signals S. Nimbhore, ..., and R. Ramteke 2013 International Conference on Communication and Signal Processing 2013 - 2 citations - Show abstract - Cite 1.6% topic match

1.6%

0.5

2013

[70] Recognition of the Tonal Words of BODO Language U. Bhattacharjee Journal Not Provided 2013 - 6 citations - Show abstract - Cite 1.6% topic match

1.6%

1.8

1996

[71] Formant analysis using mixtures of Gaussians Parham Zolfaghari and T. Robinson Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96 1996 - 51 citations - Show abstract - Cite 1.6% topic match

1.6%

0.4

2019

[72] Allpass Modeling of Phase Spectrum of Speech Signals for Formant Tracking K. Vijayan, ..., and Haizhou Li 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 2019 - 2 citations - Show abstract - Cite 1.6% topic match

1.6%

1.7

2007

[73] A Processing Method for Pitch Smoothing Based on Autocorrelation and Cepstral F0 Detection Approaches Xufang Zhao, ..., and Nguyen Minh-Quang 2007 International Symposium on Signals, Systems and Electronics 2007 - 29 citations - Show abstract - Cite 1.6% topic match

1.6%

0.2

2019

[74] Energy Separation Algorithm Based Spectrum Estimation for Very Short Duration of Speech H. Patil and Srikant Viswanath 2019 27th European Signal Processing Conference (EUSIPCO) 2019 - 1 citations - Show abstract - Cite 1.6% topic match

1.6%

3.4

1976

[75] Real-time digital hardware pitch detector J. J. Dubnowski, ..., and L. Rabiner IEEE Transactions on Acoustics, Speech, and Signal Processing 1976 - 167 citations - Show abstract - Cite 1.6% topic match

1.6%

0.0

2018

[76] Evaluating noise suppression methods for recovering the Lombard speech from vocal output in an external noise field G. Vaziri, ..., and H. Dajani International Journal of Speech Technology 2018 - 0 citations - Show abstract - Cite 1.6% topic match

1.6%

0.0

2023

[77] Mapping Phonemes to Acoustic Symbols and Codes Using Synchrony in Speech Modulation Vectors Estimated by the Travellingwave Filter Bank A. Rao Interspeech 2023 - 0 citations - Show abstract - Cite 1.6% topic match

1.6%

0.0

2008

[78] A method for simultaneously extract the fundamental frequency of a speech signal and segment it S. Rossignol and J. Collette 2008 IEEE International Symposium on Signal Processing and Information Technology 2008 - 0 citations - Show abstract - Cite 1.6% topic match

1.6%

0.2

2011

[79] A New Formant Detection Algorithm Based on Cepstrum J. He, ..., and Jianjun Hu Applied Mechanics and Materials 2011 - 3 citations - Show abstract - Cite 1.6% topic match

1.6%

0.4

2004

[80] Some Notes on Nonlinearities of Speech A. Esposito and M. Marinaro Summer School on Neural Networks 2004 - 9 citations - Show abstract - Cite 1.6% topic match

1.6%

0.4

2008

[81] Pitch tracking and voiced/unvoiced detection in noisy environment using optimal sequence estimation M. Wasserblat, ..., and Yuval Domb https://doi.org/10.1049/CP:20080636 2008 - 6 citations - Show abstract - Cite 1.6% topic match

1.6%

0.9

2018

[82] Evaluation of Automatic Formant Trackers F. Schiel and Thomas Zitzelsberger International Conference on Language Resources and Evaluation 2018 - 6 citations - Show abstract - Cite - PDF 1.6% topic match

1.6%

0.3

1998

[83] Auditory modeling techniques for robust pitch extraction and noise reduction P. Cosi, ..., and E. Zovato 5th International Conference on Spoken Language Processing (ICSLP 1998) 1998 - 7 citations - Show abstract - Cite 1.6% topic match

1.6%

1.3

2007

[84] Evaluation of Performance of Several Established Pitch Detection Algorithms in Pathological Voices S. Jang, ..., and Y. Yoon 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2007 - 22 citations - Show abstract - Cite 1.6% topic match

1.6%

2.1

2016

[85] Pathological voices detection using Support Vector Machine I. Hammami, ..., and S. Labidi 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) 2016 - 18 citations - Show abstract - Cite 1.6% topic match

1.6%

0.3

2013

[86] An artificial intelligence approach to concatenative sound synthesis N. Norowi Journal Not Provided 2013 - 4 citations - Show abstract - Cite 1.6% topic match

1.6%

4.0

2012

[87] Expressive speech synthesis: a review D. Govind, ..., and S. Prasanna International Journal of Speech Technology 2012 - 48 citations - Show abstract - Cite 1.6% topic match

1.5%

0.1

1996

[88] Vowel-non vowel classification of speech using an MLP and rules J. Sirigos, ..., and G. Kokkinakis 1996 8th European Signal Processing Conference (EUSIPCO 1996) 1996 - 3 citations - Show abstract - Cite 1.5% topic match

1.5%

0.6

2007

[89] Pitch tracking of acoustic signals based on average squared mean difference function Roudra Chakraborty, ..., and Sagnik Sinha Signal, Image and Video Processing 2007 - 11 citations - Show abstract - Cite - PDF 1.5% topic match

1.5%

1.8

2003

[90] PITCH DETECTION ALGORITHM : AUTOCORRELATION METHOD AND AMDF Li Tan and M. Karnjanadecha Journal Not Provided 2003 - 39 citations - Show abstract - Cite 1.5% topic match

1.5%

0.3

1997

[91] A formant vocoder based on mixtures of Gaussians Parham Zolfaghari and T. Robinson 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing 1997 - 9 citations - Show abstract - Cite 1.5% topic match

1.5%

1.0

2002

[92] Robust formant tracking in noise I. Bruce, ..., and M. Sachs 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing 2002 - 22 citations - Show abstract - Cite 1.5% topic match

1.5%

5.5

2011

[93] HMM-Based Multipitch Tracking for Noisy and Reverberant Speech Zhaozhang Jin and Deliang Wang IEEE Transactions on Audio, Speech, and Language Processing 2011 - 74 citations - Show abstract - Cite - PDF 1.5% topic match

1.5%

1.8

2015

[94] An m-health system for the estimation of voice disorders Laura Verde, ..., and Giovanna Sannino 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) 2015 - 17 citations - Show abstract - Cite 1.5% topic match

1.5%

5.2

1970

[95] System for automatic formant analysis of voiced speech. R. Schafer and L. Rabiner The Journal of the Acoustical Society of America 1970 - 287 citations - Show abstract - Cite 1.5% topic match

1.5%

1.7

2008

[96] Segmentation of a speech spectrogram using mathematical morphology Raphael Steinberg and D. O'Shaughnessy 2008 IEEE International Conference on Acoustics, Speech and Signal Processing 2008 - 28 citations - Show abstract - Cite 1.5% topic match

1.5%

0.7

1995

[97] Robust, n-best formant tracking Philipp Schmid and E. Barnard https://doi.org/10.21437/Eurospeech.1995-168 1995 - 19 citations - Show abstract - Cite 1.5% topic match

1.5%

2.6

2016

[98] Gender identification from speech signal by examining the speech production characteristics Esther Ramdinmawii and V. K. Mittal 2016 International Conference on Signal Processing and Communication (ICSC) 2016 - 21 citations - Show abstract - Cite 1.5% topic match

1.5%

0.2

2005

[99] An Automated Event Analysis System with Compositional Applications N. Collins International Conference on Mathematics and Computing 2005 - 4 citations - Show abstract - Cite 1.5% topic match

1.5%

12.4

1977

[100] On the use of autocorrelation analysis for pitch detection L. Rabiner IEEE Transactions on Acoustics, Speech, and Signal Processing 1977 - 592 citations - Show abstract - Cite 1.5% topic match

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