Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals
Abstract
:Simple Summary
Abstract
1. Introduction
- We propose a novel method for cleaning impaired speech signals by combining Pareto-optimized deep learning with NMF, addressing the limitations of traditional speech enhancement techniques.
- We introduce a smoothing technique for the noise-to-signal mask to avoid abrupt transitions in noise levels, resulting in a more natural-sounding output signal.
- We demonstrate the effectiveness of our approach through a series of experiments, showing significant improvements in speech quality and intelligibility compared to traditional methods.
2. Review of State-of-the-Art Works
2.1. Assessing Speech-Signal Impairments
2.2. Algorithms for Alaryngeal Speech Enhancement
3. Materials and Methods
3.1. Dataset
3.2. Alaryngeal Speech Assessment
- 1.
- The artificial intelligence-based automated classifier for substitution voicing ResNet 118 was used to assign speech samples to the following classes: normal speech—Probability 0; speech with a single vocal fold—Probability 1; and alaryngeal speech with TEP—Probability 2 [91].
- 2.
- The acoustic parameter of alaryngeal speech (average voicing evidence (AVE), available in the AMPEX software [92]) was utilized to compare the alaryngeal speech samples before and after optimization using Pareto-optimized NMF software. The AVE parameter describes the average voicing evidence and the degree of regularity/periodicity in the voiced frames. Since the actual background frames are usually unvoiced, the analysis is performed on all frames, not just speech frames. This approach is more robust against possible errors of the speech/background classification, which is purely energy-based. In contrast, the voicing evidence is derived from analyzing all the sub-band signals created by the auditory model.
- 3.
- The AI-based acoustic substitution voicing index (ASVI) parameter [93] was employed to quantitatively evaluate the alaryngeal speech samples before and after optimization using Pareto-optimized NMF software. This parameter includes the constant combined with statistically significant parameters from ResNet 118 (Probability 0, Probability 1, and Probability 2) combined with the AVE and mean fundamental frequency. The possible ASVI values ranged from 0 to 30, with better speech quality indicated by higher scores.
3.3. Methodology
3.3.1. Non-Negative Matrix Factorization (NMF)
3.3.2. Pareto-Optimized Non-Negative Matrix Factorization (PONMF)
Algorithm 1 Pareto-Optimized Deep Learning for Impaired Speech Cleaning |
|
3.3.3. Speech-Signal Cleaning
- 1.
- Calculate the spectrogram of the entire noisy voice clip. This is achieved by windowing the noisy voice clip and taking its Fourier transform over time to obtain a spectrogram, which is a representation of the frequency spectrum of a signal over time.
- 2.
- Compute the frequency statistics from the spectrogram. This is achieved by calculating the mean and standard deviation of the magnitude of each frequency bin over time. These statistics help in understanding the distribution and characteristics of the noise present in the voice clip.
- 3.
- Calculate a threshold based on the desired noise sensitivity. This threshold helps differentiate between the noise and signal components in the spectrogram.
- 4.
- Determine the signal spectrogram using the same input noisy voice clip. This is achieved by windowing the noisy voice clip and taking its Fourier transform over time.
- 5.
- Compute the noise-to-signal mask using the calculated threshold. The mask is a binary value for each frequency bin and time frame of the spectrogram, where 1 indicates the signal and 0 indicates noise.
- 6.
- Smooth the noise-to-signal mask by applying a filter in both the frequency and time domains. This helps avoid sudden jumps in noise levels and produces a more continuous and less abrupt mask.
- 7.
- Apply the smoothed mask to the spectrogram of the signal. This step effectively suppresses the noise components in the spectrogram while retaining the desired signal.
- 8.
- Decompose the modified spectrogram using Pareto-optimized non-negative matrix factorization (NMF). NMF-based methods for speech enhancement involve learning the basis functions and Pareto-optimized weights that best represent the clean speech signal.
- 9.
- Reconstruct the clean speech from the noisy input signal using the learned basis functions and Pareto-optimized weights.
- 10.
- Invert the reconstructed spectrogram to create a noise-reduced waveform. This final output is a cleaned version of the original impaired speech, with the noise components significantly reduced or removed.
3.3.4. Pareto-Optimized Deep Learning with NMF for Impaired Speech Cleaning
Algorithm 2 Pareto-Optimized Deep Learning with NMF for Impaired Speech Cleaning |
|
4. Results
- Probability 0: sig. = 0.000, indicating that the variances were not equal across groups.
- Probability 1: sig. = 0.454, indicating that the variances were equal.
- Probability 2: sig. = 0.008, indicating that the variances were not equal.
- AVE: sig. = 0.340, indicating equal variances across groups.
- ASVI: sig. = 0.166, indicating equal variances across groups.
- Probability 0: sig. = 0.036 (for equal variances assumed) and 0.037 (for equal variances not assumed), indicating that the means of the two groups were significantly different.
- Probability 1: sig. = 0.890 (both cases), indicating that the means were not significantly different.
- Probability 2: sig. = 0.163 (both cases), indicating that the means are not significantly different.
- AVE: sig. = 0.750 (both cases), indicating that the means are not significantly different.
- ASVI: sig. = 0.133 (for equal variances assumed) and 0.134 (for equal variances not assumed), indicating that the means are not significantly different.
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
NMF | non-negative matrix factorization |
UA-SPEECH | sound dataset |
GMM | Gaussian mixture model |
SVM | support vector machine |
AGMM | adjusted Gaussian mixture model |
DSP | digital signal processing |
AI | Artificial Intelligence |
LMS | least mean square |
SNR | signal-to-noise ratio |
MSE | mean square error |
PSNR | peak signal-to-noise ratio |
LPC | linear predictive coding |
CNN | convolutional neural network |
RNN | recurrent neural network |
GAN | generative adversarial network |
TEP | tracheoesophageal prosthesis |
Prob0 | probability of healthy speech |
Prob1 | probability of speech with a single vocal fold |
Prob2 | probability of tracheoesophageal speech |
AVE | average voicing evidence |
ASVI | acoustic substitution voicing index |
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Group | N | Mean | Std. Deviation | p | |
---|---|---|---|---|---|
Probability 0 | Original | 75 | 4.09 | 19.51 | 0.001 |
Pareto-optimized NMF | 75 | 13.51 | 33.3 | 0.001 | |
Probability 1 | Original | 75 | 56.18 | 48.66 | 0.454 |
Pareto-optimized NMF | 75 | 57.28 | 47.83 | 0.454 | |
Probability 2 | Original | 75 | 39.73 | 47.9 | 0.08 |
Pareto-optimized NMF | 75 | 29.21 | 43.89 | 0.08 | |
AVE | Original | 75 | 0.81 | 0.11 | 0.34 |
Pareto-optimized NMF | 75 | 0.8 | 0.1 | 0.34 | |
ASVI | Original | 75 | 8.8 | 4.94 | 0.166 |
Pareto-optimized NMF | 75 | 10.17 | 6.09 | 0.166 |
Group | Method | N | p | |
---|---|---|---|---|
Healthy speech | Original | 4 | 4.0 | 0.043 |
Pareto-optimized NMF | 10 | 13.33 | ||
Speech after laryngeal oncosurgery | Original | 72 | 96.0 | 4.097 |
Pareto-optimized NMF | 65 | 86.67 |
Levene’s Test | t-Test for Equality of Means | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
F | Sig. | t | df | Sig. (2-Tailed) | Mean Difference | Std. Error Difference | 95% Conf. Int. | |||
Lower | Upper | |||||||||
Probability 0 | Equal variances assumed | 18.313 | 0.000 | −2.113 | 148 | 0.036 | −9.41893 | 4.45670 | −18.22592 | −0.61195 |
Equal variances not assumed | −2.113 | 119.448 | 0.037 | −9.41893 | 4.45670 | −18.24330 | −0.59457 | |||
Probability 1 | Equal variances assumed | 0.563 | 0.454 | −0.139 | 148 | 0.890 | −1.09627 | 7.87862 | −16.66538 | 14.47284 |
Equal variances not assumed | −0.139 | 147.956 | 0.890 | −1.09627 | 7.87862 | −16.66542 | 14.47288 | |||
Probability 2 | Equal variances assumed | 7.317 | 0.008 | 1.402 | 148 | 0.163 | 10.51547 | 7.50161 | −4.30864 | 25.33957 |
Equal variances not assumed | 1.402 | 146.885 | 0.163 | 10.51547 | 7.50161 | −4.30957 | 25.34050 | |||
AVE | Equal variances assumed | 0.918 | 0.340 | 0.319 | 148 | 0.750 | 0.005560 | 0.017451 | −0.028926 | 0.040046 |
Equal variances not assumed | 0.319 | 147.237 | 0.750 | 0.005560 | 0.017451 | −0.028927 | 0.040047 | |||
ASVI | Equal variances assumed | 1.941 | 0.166 | −1.509 | 148 | 0.133 | −1.36607 | 0.90525 | −3.15495 | 0.42281 |
Equal variances not assumed | −1.509 | 141.961 | 0.134 | −1.36607 | 0.90525 | −3.15558 | 0.42343 |
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Maskeliūnas, R.; Damaševičius, R.; Kulikajevas, A.; Pribuišis, K.; Ulozaitė-Stanienė, N.; Uloza, V. Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals. Cancers 2023, 15, 3644. https://doi.org/10.3390/cancers15143644
Maskeliūnas R, Damaševičius R, Kulikajevas A, Pribuišis K, Ulozaitė-Stanienė N, Uloza V. Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals. Cancers. 2023; 15(14):3644. https://doi.org/10.3390/cancers15143644
Chicago/Turabian StyleMaskeliūnas, Rytis, Robertas Damaševičius, Audrius Kulikajevas, Kipras Pribuišis, Nora Ulozaitė-Stanienė, and Virgilijus Uloza. 2023. "Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals" Cancers 15, no. 14: 3644. https://doi.org/10.3390/cancers15143644